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Tandem Diabetes (TNDM) Launches Dexcom G7 Integrated t:slim X2 – December 7, 2023

Diabetes News

Tandem Diabetes Care, Inc. (TNDM Free Report) recently launched the updated t:slim X2 insulin pump software with Dexcom G7 Continuous Glucose Monitoring (“CGM”) integration in the United States. This marks the company’s flagship pump platform, t:slim X2, with the Control-IQ technology, to be the only automated insulin delivery (AID) system in the world to feature Dexcom’s most advanced CGM technology.

Tandem Diabetes’ product innovations are helping expand its reach into different customer segments of the market beyond where it operates, providing additional choices to new and existing customers to manage their diabetes. The launch of the Tandem t:slim X2 insulin pump integrated with Dexcom G7 further demonstrates the company’s commitment to continued leadership in advancing AID systems.

News in Detail

With the integration of Dexcom G7, t:slim X2 insulin pump, users can now spend more time in a closed loop with little to no wait time between Dexcom G7 CGM sensor sessions. The users are allowed even more choices in their diabetes management, with the option of using either a Dexcom G6 or a Dexcom G7 CGM sensor.

The Dexcom G7 sensor is 60 percent smaller than its predecessor, Dexcom G6, and offers a range of new features. Apart from being the most accurate, the new, discreet sensor is also the fastest CGM connected to the t:slim X2 pump, with a 30-minute sensor warmup time compared to two hours previously. Dexcom G7 also features a 12-hour grace period to replace the finished sensors for a more seamless transition between sessions and flexibility when changing sensors.

Tandem Diabetes will email instructions to all in-warranty t:slim X2 users in the United States to offer the option to add the new feature free of charge via remote software update. Pre-loaded with the updated software, t:slim X2 pumps are now being shipped to new customers.

Significance of the Launch

The Tandem Diabetes-Dexcom collaboration has entered its 10th year, and the company is focused on sustaining the rapid pace of innovation to further its mission of helping improve the lives of people with diabetes. With the latest offering, Tandem Diabetes now provides more than 300,000 current t:slim X2 users the ability to integrate with Dexcom’s most advanced CGM technology.

Outside the United States, the t:slim X2 pump with Dexcom G7 integration is expected to be launched in additional countries in early 2024.

Industry Prospects

Per a Research report, the AID system market was valued at $749.2 million in 2022 and is expected to witness a CAGR of 9.8% by 2030.

Bright Prospects of Product Innovations

It has been a transitional time for Tandem Diabetes as it prepares for its next phase of growth through the expansion of its technology offerings. The company is executing several near-term product launches while implementing scalable systems and processes to support its global operations and leverage the infrastructure.

Apart from G7, Tandem Mobi is also preparing for the launch of the t:slim X2 integration with the Abbott FreeStyle Libre 2 sensor. This new integrated offering is an incredible accomplishment, bringing the benefits of AID technology to Abbott’s customers in the United States for the first time.

In addition, Tandem Mobi is leading the way in creating a whole new category of devices for insulin therapy. The anticipation around the novel miniaturized durable pump has started to build and is already generating incredible interest among healthcare providers, people using multiple daily injections and current pumpers. Management is set to begin Mobi’s scaled launch with a limited release in the fourth quarter, followed by broad availability in early 2024.

Price Performance

In the past six months, TNDM shares have declined 8% compared with the industry’s fall of 6.2%.

Zacks Rank and Key Picks

Tandem Diabetes Care currently carries a Zacks Rank #3 (Hold).

Some better-ranked stocks in the broader medical space are Haemonetics (HAE Free Report) , Insulet (PODD Free Report) and DexCom (DXCM Free Report) . Haemonetics and DexCom each presently carry a Zacks Rank #2 (Buy), and Insulet sports a Zacks Rank #1 (Strong Buy). You can see the complete list of today’s Zacks #1 Rank stocks here.

Haemonetics’ stock has decreased 1.9% in the past year. Earnings estimates for Haemonetics have increased from $3.86 to $3.89 in 2023 and $4.11 to $4.15 in 2024 in the past 30 days.

HAE’s earnings beat estimates in each of the trailing four quarters, delivering an average surprise of 16.1%. In the last reported quarter, it posted an earnings surprise of 5.3%.

Estimates for Insulet’s 2023 earnings per share have increased from $1.85 to $1.91 in the past 30 days. Shares of the company have dropped 37.6% in the past year compared with the industry’s decline of 6%.

PODD’s earnings surpassed estimates in all the trailing four quarters, the average surprise being 105.1%. In the last reported quarter, it delivered an average earnings surprise of 77.5%.

Estimates for DexCom’s 2023 earnings per share have increased from $1.41 to $1.43 in the past seven days and to $1.44 in the past 30 days. Shares of the company have decreased 4% in the past year compared with the industry’s decline of 6.5%.

DXCM’s earnings surpassed estimates in all the trailing four quarters, the average surprise being 36.4%. In the last reported quarter, it delivered an average earnings surprise of 47.1%.

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Len Rome’s Local Health: Diabetes drugs can reduce heart issues

Diabetes News

(WYTV)- A drug we use to treat diabetes could soon have another use.

Researchers at the Cleveland Clinic found it may also be able to help reduce the risk of heart trouble in those who are not diabetic.

Could patients who are overweight or obese who don’t have diabetes find a benefit in this drug called semaglutide? Yes.

We know it worked in those with diabetes, able to reduce the risk for cardiovascular events by about 20%. The results are promising and could pave the way for future treatments.

“So, this marks the first intervention, either a lifestyle or a pharmacologic intervention, that’s ever been shown to reduce the risk of cardiovascular events in patients who are overweight and obese but don’t have diabetes,” said Dr. Michael Lincoff of the Cleveland Clinic.

The diabetic drug seems to work best in adults who are 45 and older, considered overweight or obese, not diabetic, and who have previously had a cardiovascular event.

The drug is available now but the Food and Drug Administration still has to review it for this extra use.

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Analyzing the Relationship Between LTBI and Diabetes Mellitus

Diabetes News

The following is a summary of “Diabetes mellitus and latent tuberculosis infection: an updated meta-analysis and systematic review,” published in the November 2023 issue of Infectious Disease by Zhou et al.

Prior research has established a connection between diabetes mellitus (DM) and latent tuberculosis infection (LTBI), motivating this investigation to enhance our comprehension of this association.

Researchers initiated a retrospective study employing a systematic review and meta-analysis to investigate the connection between DM and LTBI and establish a valuable reference point for future research.

They performed thorough searches across Embase, Cochrane Library, and PubMed without imposing any initial date or language limitations (July 19, 2022). This involved observational research comparing LTBI rates in DM and non-DM groups, reporting aRR or aOR results. Study quality was assessed using the Newcastle–Ottawa Scale. Pooled effect estimates with 95% CI were calculated via random-effects models.

The results showed 22 studies with a total of 68,256 subjects. Among these, three cohort studies met the eligibility criteria, yielding a combined aRR of 1.26 (95% CI: 0.71–2.23). Additionally, 19 cross-sectional studies showed a combined aOR 1.21 (95% CI: 1.14–1.29). The pooled estimate for the crude RR (cRR) from the three cohort studies was 1.62 (95% CI: 1.03–2.57). Among the cross-sectional studies in the analysis, 16 provided crude ORs, resulting in a combined crude OR (cOR) estimate of 1.64 (95% CI: 1.36–1.97). When comparing the diagnosis of diabetes, the pooled aOR for the HbA1c group exceeded that of the self-reported group (pooled aOR: 1.56, 95% CI: 1.24–1.96 vs. 1.17, 95% CI: 1.06–1.28).

They concluded that DM was linked to an increased risk of LTBI, necessitating further research and tailored public health measures.


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diabetes, claves de redacción | FundéuRAE

Diabetes News

Con motivo del Día Mundial de la Diabetes, que se celebra el 14 de noviembre, se recogen a continuación algunas claves para mejorar la redacción de las informaciones relacionadas con esta enfermedad metabólica crónica.

1. Diabetes, sin tilde

El término diabetes es una palabra llana y terminada en ese, por lo cual se escribe sin tilde. Según el Diccionario panhispánico de dudas, se desaconseja la acentuación esdrújula (diábetes), que se oye en ocasiones en algunos países, y otras formas como diabetis.

2. La denominación «diabetes mellitus», con minúscula

Diabetes es la forma abreviada del nombre completo de la enfermedad, «diabetes mellitus», escrita así, en minúsculas, como los nombres de todas las enfermedades, con mellitus con doble ele y en cursiva por ser el nombre latino: «La diabetes mellitus es un problema de salud pública a escala mundial».

3. Prediabetes, en una palabra

El prefijo pre-, que se utiliza en la formación de nombres y adjetivos, se escribe unido a la palabra a la que acompaña, sin espacio ni guion intermedios: prediabetes, y no pre diabetes ni pre-diabetes.

4. Insulinodependiente, término válido

El Diccionario de la lengua española recoge el adjetivo insulinodependiente con el significado de ‘que precisa de la administración de insulina’ y señala que también es posible usarlo como sustantivo aplicado a personas: un/una insulinodependiente. Por su parte, el Diccionario de términos médicos, de la Real Academia Nacional de Medicina de España, advierte que es incorrecta la forma insulín⁠-⁠dependiente.

5. Células madre, plural recomendado

En las noticias relacionadas con las investigaciones para mejorar la vida de los pacientes, es común el uso de la construcción células madre. En este caso, al igual que ocurre con otros sustantivos en aposición, lo habitual es mantener madre invariable en plural (y no emplear células madres).

6. Azúcar, válido en masculino y en femenino

Azúcar es un sustantivo ambiguo, es decir, se puede emplear como masculino o femenino: el azúcar, la azúcar. Si no lo acompaña ningún adjetivo, es mayoritario el empleo del masculino, mientras que, si lleva un adjetivo, predomina el femenino. En plural, lleve o no adjetivo, prevalece el empleo en masculino. Otra singularidad del término azúcar es que, sin empezar por a tónica, acepta el uso del artículo el combinado con un adjetivo femenino: «Los carbohidratos, principalmente el azúcar refinada, reducen la actividad del cerebro».

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New technology helping people in their fight against diabetes

Diabetes News

PITTSBURGH (KDKA) — November is National Diabetes Month, a condition that afflicts 133 million Americans — but like with so many things, new technology is helping fight diabetes and head off serious health problems.

KDKA’s John Shumway is here to explain and you guessed it, there’s an app for that. 

There are many apps to help you monitor your body and there’s the old finger pricking, but this goes a step beyond.

Diabetes left unchecked can be devastating and life-altering.

“It can increase risk for heart attack and stroke, risk for renal disease and renal failure, and a risk for sores that don’t heal leading to potential amputation,” said Dr. Francine Kaufman, Endocrinologist and former president of the American Diabetes Association.

Dr. Kaufman says that every diabetes patient’s goal should be to control or even reverse the disease, which they can try to achieve with optimal health and an optimal lifestyle exercising and watching their glucose levels.

Traditionally, watching your glucose level required a finger prick or a visit to the doctor for a blood test, but not anymore.

“The best way to do that is with a continuous glucose monitor,” Dr. Kaufman said. “That’s either a transcutaneous monitor or there is an implanted monitor.”

They are monitors that are actually just under your skin and constantly monitoring your levels, and the cool part is that they send data to a cell phone or to a monitor and then also to a smart watch, so you can keep an eye on it yourself and watch your values throughout the day. 

Knowing your values throughout the day can help you make adjustments if your numbers get out of whack.

There are a lot of treatments for diabetes but you need to know where you are first — and being proactive can prevent your condition from deteriorating and causing some serious problems.

The implant that Dr. Kaufman is talking about is a tiny sensor that is inserted under your skin by a doctor and has to be replaced every six months.

The surface monitor, you put on yourself and does penetrate the skin and is held in place by adhesive and is good for a couple of weeks. 

They are typically covered by insurance. 

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New American Diabetes Association Report Finds Annual Costs of Diabetes to be $412.9 Billion

Diabetes News

Although diabetes prevalence remains stable, the direct medical costs attributed to diabetes increased by 7% between 2017 and 2022.

ARLINGTON, Va., Nov. 1, 2023 /PRNewswire/ — Today, the American Diabetes Association® (ADA) published the Economic Costs of Diabetes in the U.S. in 2022 (Economic Report), a comprehensive analysis assessing the financial burden of living with diabetes in the United States. The Economic Report, which is published every five years, found that the total annual cost of diabetes in 2022 is $412.9 billion, including $306.6 billion in direct medical costs and $106.3 billion in indirect costs. People with diagnosed diabetes now account for one of every four health care dollars spent in the U.S.

“We now know that medical costs for people living with diabetes increased by 35% over the past 10 years. The ADA’s Economic Report reaffirms that, in addition to its enormous physical and health burden, diabetes also carries an untenable cost burden that is often disproportionately borne by vulnerable and underserved communities,” said Charles “Chuck” Henderson, the ADA’s CEO. “Reducing the cost of diabetes is essential to improving the lives of all people with diabetes. November is American Diabetes Month®, and as we continue our fight to end diabetes, we urge policymakers and the entire health care system to see this report as a call to action to prioritize affordable diabetes care.”

The Economic Report includes data on diabetes prevalence, total direct medical costs, and average annual medical expenditures. Primary cost drivers include increased use of prescription medications beyond glucose lowering medications, hospital inpatient services, reduced work productivity, and unemployment.

Additional topline findings include:

  • In 2022, it is estimated that 25.5 million people in the U.S. have diagnosed diabetes, approximately 7.6% of the total U.S. population.
  • The estimated number of deaths attributable to diabetes in 2022 is 339,000.
  • After adjusting for inflation, the direct medical cost of diabetes increased by 7% between 2017 and 2022.
  • National health care costs attributable to diabetes have increased by $80 billion in the past 10 years—from $227 billion in 2012 to $307 billion in 2022.
  • On average, people with diagnosed diabetes have medical expenditures 2.6 times higher than would be expected without diabetes.
  • The inflation adjusted cost of insulin increased 24% from 2017 to 2022.
  • Spending on insulin tripled in the past 10 years—increasing from $8 billion in 2012 to $22.3 billion in 2022.
  • After adjusting for inflation, the total cost of insulin and other medications to manage blood glucose increased by 26% from 2017 to 2022.
  • Despite having a lower prevalence rate, women with diabetes spend more on average than men on annual health care expenditures.
  • Black Americans with diabetes pay the most in direct health care expenditures.
  • People with diabetes above the age of 65 spend roughly double on per capita annual health care expenditures than any other age group above the age of 18.
  • $106.3 billion (26%) of the total estimated national cost of diabetes can be attributed to lost productivity at work, unemployment from chronic disability, and premature mortality.
  • Presenteeism, or reduced work productivity, accounts for $35.8 billion in annual indirect costs.
  • Absenteeism, or missed workdays, accounts for $5.4 billion in annual indirect costs.
  • If people with diabetes participated in the workforce like peers without diabetes, there would be 2 million more people between the ages of 18 and 65 in the workforce.

The authors of the Economic Report included a multidisciplinary team of leading U.S. experts in the field of diabetes care and costs, including physicians, epidemiologists, endocrinologists, health care researchers, economists, data scientists, and academics.

The full Economic Costs of Diabetes in the US in 2022 report is available online and will appear in the December issue of ADA journal, Diabetes Care®.

About the American Diabetes Association

The American Diabetes Association (ADA) is the nation’s leading voluntary health organization fighting to bend the curve on the diabetes epidemic and help people living with diabetes thrive. For 83 years, the ADA has driven discovery and research to treat, manage, and prevent diabetes while working relentlessly for a cure. Through advocacy, program development, and education we aim to improve the quality of life for the over 133 million Americans living with diabetes or prediabetes. Diabetes has brought us together. What we do next will make us Connected for Life®. To learn more or to get involved, visit us at call 1-800-DIABETES (1-800-342-2383). Join the fight with us on Facebook (American Diabetes Association), Spanish Facebook (Asociación Americana de la Diabetes), LinkedIn (American Diabetes Association), Twitter (@AmDiabetesAssn), and Instagram (@AmDiabetesAssn).  

Contact: Virginia Cramer, (703) 253-4927
[email protected] 

SOURCE American Diabetes Association

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Diabetes complications worsen prognosis for colorectal cancer patients

Diabetes News

Complications of diabetes can have numerous negative health effects, from impaired vision and nerve damage to kidney dysfunction and heart disease. In an analysis of information on adults with colorectal cancer, patients who also had diabetes—particularly those with diabetic complications—faced a higher risk of dying early. The results are published by Wiley online in CANCER, a peer-reviewed journal of the American Cancer Society.

For the study, Kuo‐Liong Chien, MD, PhD, of National Taiwan University, and his colleagues examined data registered between 2007 and 2015 in the Taiwan Cancer Registry Database, which is linked to health insurance and death records. Their analysis included 59,202 individuals with stage I–III colorectal cancer who underwent potentially curative surgery to remove their tumors. Among these patients, 9,448 experienced a cancer recurrence and 21,031 died from any cause during the study period.

Compared with individuals without diabetes, those with uncomplicated diabetes were at a minimally or insignificantly higher risk of all‐cause and cancer‐specific death, whereas those with complicated diabetes had 85% higher odds of death from any cause and 41% higher odds of death from cancer. These associations were more pronounced in women and in patients with early‐stage colorectal cancer.

Also, compared to patients without diabetes, patients with uncomplicated or complicated diabetes had a 10–11% higher risk of colorectal cancer recurrence.

The mechanisms behind the relationship between diabetic severity and poor colorectal cancer prognosis could involve various pathways and responses triggered by high insulin and glucose levels in the blood, as well as elevated inflammatory states, which are characteristic of type 2 diabetes.

While a higher diabetes prevalence was noted in patients with colorectal cancer, the study suggests that coordinated medical care involving multiple specialists can help prevent diabetes complications, potentially improving long-term colorectal cancer oncological outcomes, particularly in women and patients with early-stage cancer.”

Kuo‐Liong Chien, MD, PhD, National Taiwan University


Journal reference:

Hsu, H. Y., et al. (2023) Diabetic severity and oncological outcomes of colorectal cancer following curative resection: A population-based cohort study in Taiwan diabetes and colorectal cancer prognosis. Cancer.

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Cardiac Autonomic Neuropathy in Newly Diagnosed Patients With Type 2 Diabetes Mellitus

Diabetes News

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Factors in development of gestational diabetes mellitus

Diabetes News


Gestational diabetes mellitus (GDM) is characterized by glucose intolerance during pregnancy.1 The prevalence of GDM is 5.8–25.5% worldwide.2 The reasons for such increase include delayed pregnant age, obesity, and family history of type 2 diabetes mellitus (T2DM).2,3 GDM contributes to many perinatal complications and increases the risk of future maternal and infant metabolic diseases in the future.3

GDM is usually detected using the oral glucose tolerance test (OGTT) and diagnosed during 24–28 weeks of pregnancy, leaving a limited time for intervention. Thus, it is important to identify the risk of GDM by using a simple and practical method during the first trimester in clinical practice. Several studies have investigated GDM prediction models.4–6 However, few studies have considered the clinical indicators, glucose and lipid metabolism, and body composition into consideration together.

Obesity before pregnancy is a risk factor for the development of GDM. Although body mass index (BMI) is widely used, it does not distinguish fat mass (FM) or skeletal muscle mass (SMM). Maternal obesity and visceral adipose tissue (VAT) are associated with insulin resistance and metabolic disorders7 and are good predictors of GDM risk.8,9 However, some pregnant women are lean, and inadequate muscle mass might be the reason because muscles participate in glucose metabolism and are associated with insulin resistance.10 Therefore, identifying and qualifying body composition in early pregnancy is important for predicting GDM.

Glucose and lipid metabolism are associated with GDM.11,12 Glycated albumin (GA) is the product of non-enzymatic glycosylation of plasma albumin and reflects the blood glucose level in the preceding 2–3 weeks,13 which is more sensitive to glucose monitoring than HbA1c.14 Thus, it is widely used to monitor glucose control during pregnancy, but the predictive ability of GA in the first trimester for GDM has not been fully studied. Maternal lipid metabolism increases with gestational age physiologically, but excessive TG level contributes to subsequent GDM development.11,15

Abnormal body composition, glucose, and lipid metabolism contributed by undesirable dietary habits and sedentary lifestyles could be intervened; therefore, it is important to identify the risk factors for GDM. In this study, we investigated the relationship between glucose and lipid metabolism, body composition measured using mBIA during early pregnancy, and the development of GDM.

Materials and Methods

Study Population

This was a prospective, observational study; 20–45 years old singleton pregnant women who visited the Obstetrics Department of Peking Union Medical College Hospital for routine appointments were invited to participate in the study if they were between 6 and 12 weeks of gestation. Women were excluded if they had pre-existing diabetes and other endocrine diseases (eg, asthma, abnormal thyroid function, Cushing’s syndrome, PCOS), corticosteroid adoption, severe internal and external diseases (eg, hypertension, cardiovascular diseases, hematological diseases, renal diseases, and liver function abnormality), or severe operation history in the recent half-year. All pregnant women were followed-up until they completed a 75-g OGTT at 24–28 weeks of gestation. Subjects who could not complete the blood test, body composition test, or follow-up were excluded from the study. This study was approved by the Ethics Committee of Peking Union Medical College Hospital (No. ZS-1703), and the study complies with the Declaration of Helsinki. All participants provided written informed consent before participation. This study was registered at ( (NCT 04550806).

The sample size was calculated using the Power Analysis and Sample Size (PASS) 11.0 software with Tests for Two Proportions, where α= 5%, power=80%, RR=2.04,9 and dropping rate=10%. In total, 295 participants were included in this study.

Data Collection

Data on age, pregnancy weeks, family history of T2DM, medical history, and GDM history were collected from medical records, and weight before pregnancy was self-reported. At the first prenatal visit, weight and body composition were measured using multi-frequency bioelectrical impedance analysis (mBIA, Inbody 770, Inbody, Seoul, Korea). All participants were instructed to be in a fasting state (consumption of water was allowed until 2-hour before testing) and abstain from strenuous exercise for 48 hours prior to measurement. They removed jewellery and heavy clothing, and stood on the mBIA machine with bare feet. Fasting blood samplings, including fasting plasma glucose (FPG), GA, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglyceride (TG) levels, were collected in the morning and measured by the clinical lab of our hospital within 1 hour of blood collection. The plasma lipids and GA levels were detected by using the commercial enzymatic assays (Sekisui, Japan; Asahi Kasei Pharma Corporation, Japan), and glucose levels were detected by applying an automated analyzer (Beckman Coulter, US). Blood specimens were retained for seven days for necessary repeat if the result was irregular. GDM was diagnosed via 75-g OGTT test using the 2010 International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria when one or more of the following conditions were at or above the thresholds: FPG 5.1 mmol/L, 1-h plasma glucose 10.0 mmol/L, and 2-h plasma glucose 8.5 mmol/L.1 All data were collected, recorded, and organized by two researchers to avoid mistakes.

All participants were provided with healthy lifestyle advice by a qualified dietitian to fulfill the pregnancy requirement of appropriate energy (25–35 kcal/kg/day depending on their pre-BMI), high-quality protein, nutrients, and to avoid unhealthy food.16 Nutritional counseling was conducted after screening at admission; therefore, the grouping remained unknown.

Statistical Analysis

Normality tests were performed to determine whether the data were parametric or not. Normally distributed measurement data are presented as mean ± standard deviation (SD), and the differences between the GDM and non-GDM groups were examined using Student’s t-test. Non-normally distributed data are presented as median and interquartile range (IQR), and the difference between the two groups was examined using the Mann–Whitney test. Categorical variables were compared using the chi-square test (χ2).

Spearman’s rank correlation analysis was used for investigating the relationships between GDM and clinical data. Factors found to be statistically significant according to univariable Poisson regression (P≤0.10) or considered clinically important based on previous studies were included into multivariate Poisson regression analysis. The strength of the association between variables and GDM was expressed as relative risk (RR) with corresponding 95% confidence intervals (95% CI). The performance of the final optimized prediction model was evaluated using receiver operating characteristic (ROC) curves to estimate the area under the curve (AUC) and 95% CI. The cut-off value of relative variables was calculated based on ROC. All analyses were performed using SPSS (version 18.0; SPSS Inc., Chicago, IL, USA). A two-sided P<0.05 was considered statistically significant.



From October 2020 to January 2022, 323 pregnant women were included, but 17 lacked data and were excluded from the analysis; two pregnant women had fetal arrest, and two were lost to follow-up. The recruitment for this study was based on the STROBE guidelines,17 and 302 participants were included in the final analysis. The median (IQR) duration of follow-up of participants was 117.3 (IQR 99.5–139.1) days. Fifty-nine women (19.5%) developed GDM and were included in the observational group, while patients with normal glucose tolerance (NGT) were included in the control group (Figure 1).

Figure 1 Flowchart showing recruitment in the study according to STROBE guideline.17

Demographic Characteristics

The characteristics of all participants according to the GDM diagnosis are shown in Table 1. Women with GDM were older (P=0.008) and had higher pre-BMI (P=0.006) and TG (P=0.017) than the NGT group, while TC (P=0.066), LDL-C (P=0.310), and HDL-C (P=0.426) levels did not differ between the two groups. There was no significant difference in GA (P=0.287) or FPG (P=0.058) during the early gestational weeks either. Subjects with or without GDM, in terms of weight gain (WG) or percentage of WG within the previous month, were similar. The percentage of positive history of GDM and a family history of T2DM was higher in the GDM group than in the NGT group (P<0.05). However, the percentage of in vitro fertilization (IVF) pregnancy or macrosomia delivery history did not differ between the two groups. The 0, 1-hour, and 2-hour glucose levels were significantly higher in subjects with GDM than in those with NGT in the OGTT (P<0.001).

Table 1 Characteristics of GDM and Non-GDM Group

With respect to body composition, FM, body fat ratio (BFR), phase angle (PA), and visceral fat area (VFA) at early gestational weeks were significantly higher in the GDM group (P=0.003, 0.030, 0.003, and 0.005, respectively). Although SMM was not significantly different, SMI and SMM/FM were lower in the GDM group (P=0.046, P<0.001) (Table 1).

Risk Factors Associated with the Development of GDM

Correlation analysis showed a significant correlation between GDM and age, pre-BMI, TG, FM, BFR, VFA, SMM, SMI, SMM/FM, PA, and a family history of T2DM (Table 2). Adjusted for covariates, such as age, pre-BMI, family history of T2DM, and GDM history, there was a positive correlation between GDM diagnosis and TG and BFR, and a negative correlation between GDM and SMI and SMM/FM. Moreover, neither GA nor FPG levels in early pregnancy correlated with GDM diagnosis. According to ROC curve, AUC of SMM/FM was 0.629 (0.547, 0.712), and the cut-off values of SMM/FM was 1.305. Therefore, we compared the incidence of GDM in the groups with SMM/FM <1.305 and ≥1.305, and found a percentage of 34.4% and 15.7%, respectively.

Table 2 Pearson’s Correlation Analysis Associated with GDM Diagnosis

The associations between age, pre-BMI, FPG, TG, BFR, VFA, SMI, SMM/FM, GDM history, IVF pregnancy, and family history of T2DM and GDM diagnosis were significant (all P<0.05) according to Poisson regression analysis, and all of these significant variables were entered into the multivariable model (see Table 3). In multivariable Poisson regression model, for each unit increase in age and pre-BMI, there was 7.6% (95% CI 1.005–1.152) and 1.2% (95% CI 1.005–1.063) increase in the risk of GDM. Simultaneously, the RRs for TG and family history of T2DM were 4.052 (95% CI 1.641–6.741) and 1.496 (95% CI 1.014–2.667). SMM/FM was a protective factor for GDM (RR 0.213 (95% CI 0.051–0.890)). In contrast, RRs for VFA, SMI, IVF pregnancy and GDM history were null in multi-factor regression analysis. Although GDM history was not significant in this study, it was regarded as a strong clinical indicator of GDM development; thus, it was included in the regression model (Table 3). According to ROC curve, the cut-off values of TG were 110.95cm2.

Table 3 Poisson Regression Analysis

The predictive model was statistically significant (χ2 = 37.743, P<0.001), explained 33.2% (R2) of the variance in GDM, and correctly classified 83.4% of the cases. The performance (AUC) of the prediction model was 0.806 (95% CI 0.737–0.895, P<0.001), with 77.8% sensitivity and 67.8% specificity. The predictive ability of the variables to classify between the GDM and NGT groups was better than that of the conventional risk model with an AUC of 0.673 (95% CI 0.576, 0.770), which included age, pre-BMI, GDM history, family history of T2DM, and macrosomia delivery history (Figure 2). Sensitivity and specificity terms for individual predictive variables. ROC curves were generated for age, pre-BMI, VFA, SMM/FM, and TG; however, any individual variable showed poor discrimination (Table 4).

Table 4 AUC for Variables Computed with ROC Analysis for GDM

Figure 2 ROC curves for the accuracy of the GDM prediction model.

Influencing Factors of GA

Because the negative results of GA for GDM prediction were rather unexpected, we analysed some of the influencing factors of GA. The univariate correlation analysis showed a negative correlation between the pre-BMI, lipids, FM, FR, VFA, SMM, SMI, PA with GA (all P≤0.01). After adjustment for age and pre-BMI, the negative correlation between TC, VFA, SMI, PA and GA remained (P<0.05) (Supplementary Table 1).


This study reported a high incidence of GDM and explored the predictive effects of glucose and lipid parameters, and body composition measurements for GDM. Lower SMM/FM and higher TG levels were risk factors for GDM; however, FPG or GA in early gestation was not associated with GDM development.

First, the incidence of GDM was found 19.5%, which was comparable to a previous report and should be awaited because Chinese individuals are at a high risk for GDM.18 We confirmed that conventional risk factors were associated with GDM development, consistent with previous studies5 and clinical practice observations. However, owing to the limited number of events, IVF pregnancy or macrosomia delivery history was not found to be significantly related to GDM in this study.

Second, body composition is associated with the risk of developing GDM. Several studies have shown that FM, VFA, subcutaneous adipose tissue thickness, and body fat index are good markers of GDM development.4,9 Here, we found that higher FM and VFA levels correlated with GDM after adjusting for pre-BMI and age. Moreover, adipose tissue, especially VFA, is associated with inflammation and insulin resistance,7 making people more likely to develop abnormal glucose metabolism. However, in the Poisson regression analysis, VFA in early pregnancy was no longer a risk factor for GDM development in this study. Skeletal muscle is responsible for the greatest insulin-stimulated glucose disposal in the body10 and is associated with the risk of diabetes.19 In addition, skeletal muscle regulates systemic insulin sensitivity through the secretion of myokines, such as musclin, IL-6, and TNFα.19,20 There are several measurements of muscles in practice, including absolute SMM, SMI, and the percentage of SMM. In our study, lower SMI than SMM was a risk factor for GDM development. Notably, adipose tissue and skeletal muscle have opposite roles in insulin sensitivity,19 and there is a positive correlation between FM and SMM (r=0.628, P=0.014, results not shown); therefore, we further analyzed the ratio of SMM to FM and confirmed the negative relationship between GDM and SMM/FM, which was also positively associated with insulin sensitivity.10 Fasting insulin levels were found to be significantly higher in GDM subjects than in NGT ones.21 Therefore, SMM/FM is useful in predicting GDM and may be a target for nutritional interventions. The cut-off value of SMM/FM from this study was 1.305, thus we will verify the validity of this cut-off value in larger sample sizes and more centers in further study. Although body composition measurement via mBIA in pregnant women is not a part of routine clinical practice, we can still do the manual measurements for muscle and fat mass, such as calf circumference, mid-upper arm circumference, waist circumference, etc.

Third, we identified an association between TG levels and GDM development, even after adjusting for age and pre-BMI. Although we further adjusted for BFR and VFA, TG levels remained associated with GDM development (r=0.212, P=0.009). Several studies suggested TG could be a risk factor for GDM.11,15 In this study, the cut-off value was 0.925 mmol/L, which is lower than the reference range for healthy adult, indicating it may be necessary to set lower standards for maternal lipids in early pregnancy to avoid possible insulin resistance and GDM. These findings, including higher adiposity and lipid levels, presumably reflect the potential pathophysiological pathways of GDM, which include the onset of insulin resistance, chronic inflammation, and adipocytokines,11,22 but maternal lipids are not yet recognized as risk factors, nor are they currently targeted for intervention in early gestation.

Muscle mass has a positive effect on insulin sensitivity, but such a protective association seems to work among lean subjects but not in subjects with excessive fat.19 Cross-talk between muscle and fat via inflammatory factors and insulin resistance has been observed,23 reflecting the importance of intervention for both muscle and fat, as well as glucose and lipid metabolism. As an important organ for energy consumption, low SMM results in low energy expenditure; thus, fat accumulates from excessive energy. In addition to regulating glucose metabolism, the skeletal muscle influences fatty acid uptake and oxidation via peroxisome proliferator-activated receptor (PPAR)-α, PPARγ coactivator 1α, glucose transporter 4 (GLUT4), and cyclic adenosine monophosphate (cAMP) response element-binding protein.24 Thus, metabolic alterations in skeletal muscle strongly affect glucose and lipid homeostasis. Moreover, the increase in VAT and insulin resistance results in a higher release of free fatty acids (FFA) into circulation, resulting in higher synthesis of TG, toxicity in the muscle, and insulin resistance,23 resulting in a reduced capacity for glucose and lipid metabolism (Figure 3). In this study, when we conducted the ROC of individual risk factors of GDM, the AUCs were lower than 0.7, whereas when we combined the factors, the prediction power improved, which was higher than the AUC of body composition alone in a Chinese population (0.672).9 Although some previous studies may have obtained higher AUCs for GDM prediction, manual body composition measurements conducted in some studies are time-consuming,4 and some biomarkers are not routinely tested in clinics.5,6

Figure 3 Relationships between adipose tissue, skeletal muscle, blood glucose and TG. This figure summarizes the relevance of inadequate muscle to increased blood glucose and FFA, and insulin resistance via inflammation and cytokines; and the relevance of excessive of adipose tissue to the increased TG and insulin resistance via inflammatory pathways. High level of FFA can cause hypertriglyceride, insulin resistance, and toxicity to muscle directly. Poor glucose control will lead to muscle loss.

Although GA and FPG were widely adopted in clinics for glucose assessment, GA or FPG in early pregnancy was not a good indicator for GDM in our study and previous studies.25,26 It might be due to the physiological fluctuation of maternal glycemia in the first trimester.27 Furthermore, GA was found to be associated with BMI, BFM, VFA, and SMM, so body composition and chronic inflammation status might be another influencing factors.

This study is the first to report the role of SMM/FM in predicting GDM and to emphasize the importance of skeletal muscle and fat in GDM development. The current predictors for GDM provide important pathophysiological insights into GDM and potential treatment targets for nonglycemic intervention, and also arouse the attention of obstetricians and dietitians to consider glucose and lipid metabolism, weight, muscle, and fat change simultaneously. In addition, the required sample size was calculated in advance, and the blinding process between mBIA measurement, blood tests, and OGTT was performed, making the study reliable.

The study was performed at a single center and was limited to a single ethnicity; thus, the results may not be generalizable to other populations. Owing to the limitations of mBIA, body composition may not be accurate; however, it is safer and more convenient than CT or DEXA for pregnancies and is widely used in clinics. The study design did not include follow-up in the second trimester of pregnancy; if this was done, we could evaluate the role of the change in variables in GDM development. Moreover, although we found a high risk of inadequate muscle for GDM development and hypothesized the potential role of insulin resistance, fasting insulin and homeostatic model assessment (HOMA) were not evaluated, which could be done in future studies.


SMM/FM, VFA, and TG levels are risk factors for GDM development, emphasizing the importance of improving body composition and lipid metabolism in early pregnancy, and could be used as important indicators of nutritional intervention for pregnant women with GDM risk.

Data Sharing Statement

The data underlying this study are available for ten years from the corresponding author upon reasonable request.

Author Contributions

FW and YB conducted the study. All authors contributed to data analysis, drafting, and revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.


This work was supported by National High Level Hospital Clinical Research Funding (no. 2022-PUMCH-B-055). The funding sources had no role in the study design, data collection, analysis, or writing of this report. We would like to thank all the participants of this study for their collaborative trials.


The authors declare no conflicts of interest.


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Daughter’s diabetes diagnosis a lesson in fortitude

Diabetes News
The Architect, shown here at age 6, spent two days in the hospital following her diagnosis with type 1 diabetes.

The Architect, shown here at age 6, spent two days in the hospital following her diagnosis with type 1 diabetes.

Four years ago today, I was a ball of nerves — calm on the outside, exploding within — as I drove our then-6-year-old to an appointment with our family doctor because a few things just weren’t right. She was always so thirsty; always so hungry. (The latter, actually, was nothing new, but the constant requests for water and subsequent bathroom breaks were definitely out of the norm.)

She’d always been built like a beanpole, but I’d noticed in recent weeks the way her knees stuck out from her long, lean legs in a way that felt … wrong, somehow. I chalked it up to a growth spurt, refusing to let my mind entertain other possibilities.

Abbey’s Road:Making kindness count

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