case study diabetes mellitus type 2

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Type 2 diabetes: a case study

Affiliation.

• 1 Queen's University Belfast, Belfast, Northern Ireland.
• PMID: 25270482
• DOI: 10.7748/ns.29.5.37.e9142

Increased prevalence of diabetes in the community has been accompanied by an increase in diabetes in hospitalised patients. About a quarter of these patients experience a hypoglycaemic episode during their admission, which is associated with increased risk of mortality and length of stay. This article examines the aetiology, pathophysiology, diagnosis and treatment of type 2 diabetes using a case study approach. The psychosocial implications for the patient are also discussed. The case study is based on a patient with diabetes who was admitted to hospital following a hypoglycaemic episode and cared for during a practice placement. The importance of early diagnosis of diabetes and the adverse effects of delayed diagnosis are discussed.

Keywords: Blood glucose; case study; diabetes; glucose testing; hyperglycaemia; hypoglycaemia; insulin resistance; sulfonylureas; type 2 diabetes.

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A case study of type 2 diabetes self-management

1 Department of Biomedical Engineering, Texas A&M University, College Station, Texas, 77843-3120 USA

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Associated Data

It has been established that careful diabetes self-management is essential in avoiding chronic complications that compromise health. Disciplined diet control and regular exercise are the keys for the type 2 diabetes self-management. An ability to maintain one's blood glucose at a relatively flat level, not fluctuating wildly with meals and hypoglycemic medical intervention, would be the goal for self-management. Hemoglobin A1c (HbA1c or simply A1c) is a measure of a long-term blood plasma glucose average, a reliable index to reflect one's diabetic condition. A simple regimen that could reduce the elevated A1c levels without altering much of type 2 diabetic patients' daily routine denotes a successful self-management strategy.

A relatively simple model that relates the food impact on blood glucose excursions for type 2 diabetes was studied. Meal is treated as a bolus injection of glucose. Medical intervention of hypoglycaemic drug or injection, if any, is lumped with secreted insulin as a damping factor. Lunch was used for test meals. The recovery period of a blood glucose excursion returning to the pre-prandial level, the maximal reach, and the area under the excursion curve were used to characterize one's ability to regulate glucose metabolism. A case study is presented here to illustrate the possibility of devising an individual-based self-management regimen.

Results of the lunch study for a type 2 diabetic subject indicate that the recovery time of the post-prandial blood glucose level can be adjusted to 4 hours, which is comparable to the typical time interval for non-diabetics: 3 to 4 hours. A moderate lifestyle adjustment of light supper coupled with morning swimming of 20 laps in a 25 m pool for 40 minutes enabled the subject to reduce his A1c level from 6.7 to 6.0 in six months and to maintain this level for the subsequent six months.

Conclusions

The preliminary result of this case study is encouraging. An individual life-style adjustment can be structured from the extracted characteristics of the post-prandial blood glucose excursions. Additional studies are certainly required to draw general applicable guidelines for lifestyle adjustments of type 2 diabetic patients.

It is well established that diabetes can lead to acute and chronic complications, compromising the health and quality of life. Results from various studies [ 1 ] have demonstrated that improved control of blood glucose in type 2 diabetes reduces related complications. Type 2 diabetes results from the metabolic problem that is related to certain tissue resistance to insulin action and to the inability of the pancreas to appropriately regulate the quantity of insulin for glucose metabolism. These metabolic abnormalities lead to the many complications of diabetes. Type 2 diabetes historically occurs predominantly in adults aged 40 and over. A recent trend, however, indicates that children and adolescents of minority ethnic groups, especially in African Americans and American Indians, are increasingly susceptible to type 2 diabetes [ 2 ]. With the prevalence of type 2 diabetes and its associated risk for serious complications, issues related to proactive self-management become an urgent concern.

Dietary management is frequently referred as the cornerstone, or the initial step, in treating of type 2 diabetes mellitus. Foods containing carbohydrates play an important role in the diet. The glycemic Index (GI) ranks foods according to their post-prandial glycemic responses. The GI was introduced more than twenty years ago and has been widely adopted in diabetes management in Australia, New Zealand, Canada, the United Kingdoms, and France [ 3 ]. The World Health Organization states that it is important to consider the GI in constructing a healthful diet because low GI foods help control blood sugar levels by producing minimal fluctuations in blood glucose [ 4 ]. For diabetic patients, choosing low GI foods is particularly important because consumption of high GI foods often results in far more exaggerated glycemic responses, creating a need for drug or insulin therapy [ 3 , 5 ].

Most published GI lists are for single food items only. A GI is a numerical measure of how a carbohydrate would increase one's blood glucose level over a period of two (for normal) or three hours (for diabetic patients) after eating [ 6 , 7 ]. The area of elevated blood glucose level from the baseline (the pre-prandial measure) is expressed as a percent of the area for the same amount of a reference carbohydrate such as a pure glucose or a white bread (usually 50 g) [ 8 , 9 ]. To plan a complete meal using the weighted mean [ 6 ] for various food items is not only tedious, but also impractical.

Diet exchange lists are usually recommended for diabetic patients to use in formulating a sensible meal plan. However, an exchange list is not always convenient to use. Moreover, there is a lack of ethnic diet exchange lists. For a member of an ethnic minority to follow a diet exchange list, he or she must prepare his or her own meal away from the rest of the family. Nutall and Chasuk [ 10 ] have stressed that dietary recommendations for type 2 diabetes should be flexible and highly individualized, yet most of the prepared meal programs and exchange-list diets for diabetes have not had individualization in mind nor are they designed for ethnic minorities.

When diet alone cannot effectively control the type 2 diabetic conditions, medical interventions, such as insulin injections or dispensing hypoglycaemic pills, are usually the next step of managing type 2 diabetes mellitus. Medical interventions notoriously exacerbate the fluctuation of blood glucose excursions. Even with the smallest dosage of hypoglycaemic drug (5 mg glucotrol or glyburide) once in the morning, the subject of this study still experienced frequent acute hypoglycaemias. Besides, his A1c levels hovered around 6.5 levels for many years following his physician's advice of taking 5 mg glucotrol per day. It became obvious that a properly designed drug dispensing regimen was needed to avoid hypoglycaemic bouts and effectively reduce A1c levels.

Fasting blood glucose measurements are not consistent indicators, fluctuating widely from a low of 70 mg/dL to a high of 200 mg/dL (with most frequent range lay between 90 to 150 mg/dL) that were experienced by this type 2 diabetic subject prior to the model-based lifestyle adjustment. Initially, the subject tried to adjust lifestyle based on fasting glucose measurements, but it was not successful. His A1c measurements crept from 6.3 to 6.7 in a year. As glucose binds irreversibly to haemoglobin molecules within red blood cells, the amount of glucose that is bound to haemoglobin is directly tied to the concentration of glucose in the blood. The average life span of erythrocytes is about 120 days [ 11 ], measuring the amount of glucose bound to haemoglobin – by the A1c measurement – can provide an estimate of average blood sugar level during the 3 to 4 months period. It is obvious that A1c is a more reliable indicator than fasting glucose measurements for an effective blood glucose control self-management.

It has been established that exercise can effectively alleviate diabetic conditions. Although no rigorous investigation has been performed here, nor is the focus of this current study, a forty-minute exercise of swimming, or weight lifting, or jogging, or any combination of these, prior to a meal or 3 to 4 hours after a meal, can significantly depress the volunteer's post-prandial blood glucose levels. However, it is impractical to substitute hypoglycemic pills with a multiple daily exercise schedule. A sensible lifestyle adjustment is required to manage the diabetic conditions without altering much of daily routines.

Post-prandial blood glucose excursions (time series) for type 2 diabetes vary widely depending on the variety and the amount of food consumed. It also depends on long and short term physical conditions (exercise routines and stress levels such as insomnia) to a lesser scale. The recovery periods of blood glucose excursions returning to the pre-prandial level (or baseline) for diabetics are generally longer than those for non-diabetics. Although a simple glucose-insulin interaction compartmental model exists [ 12 ], not all the model parameters are readily interpretable. In addition, no case study is given to illustrate its potential applications. Compartmental models can provide first-order approximations that may be sufficient for specific goals. Simple models may not duplicate real phenomena but may reveal enough clues for which alternative approaches or experimental designs may come to light.

A biophysically-based model of impulse-force-generated heavily damped oscillatory system is used here to capture the post-prandial blood glucose characteristics of type 2 diabetes. The model follows the general approach of glucose-insulin interaction model (bolus injection of glucose) with a few modifications, for which parameters can readily be interpreted and a case study is presented for exploring its potential applications. Rather than using single food items for their published GI values, or its cumbersome weighted mean of multiple ingredients in a meal, normally consumed lunch for the subject was used for the test meal. Based on the preliminary results obtained from the model, a moderate lifestyle adjustment was devised for the subject: swimming 20 laps for 40 minutes in a 25 m pool in the morning and dispensing 1/4 of 5 mg glyburide 1/2 to 1 hour before lunch and dinner – that enables him to reduce 10% of his A1c level in six months and maintain the desirable lower level for the subsequent six months.

The subject is a mid-sixty healthy male of 180 lbs with 5'10" frame, leading a productive professional life. He has been diagnosed with type 2 diabetes for more than 30 years. Initially, he was on diet regimen for nearly twenty years and then was instructed by his physician to dispense 5 mg glucotrol once every morning. He experienced frequent acute hypoglycemia that led him to discuss a possible self-managed regimen with his family physician.

Lunch was chosen as the test meal for having sufficient time to take post-prandial measurements. The test meals were 15 sets of lunches that consisted either (1) 10 to 12 oz of steamed rice, stir-fried vegetables with 4 oz canned tuna (or steamed cod), or (2) 10 to 12 oz spaghetti with 6 medium sized meat balls (from Sam's family package). Five sets of data each were collected from: (i) without taking hypoglycemic pills before test meals; (ii) 1/4 size of 5 mg glyburide pills were dispensed pre-prandially right before the meal and (iii) 1/4 size of 5 mg glyburide pills were dispensed pre-prandially an hour before the test meals. One pre- and 8 to 12 post-prandial blood glucose measurements were taken at 30-minute intervals starting at the beginning of a meal (meal is usually consumed in 15 minutes): (i) for 6 hours, (ii) for 5 hours, and (iii) for 4 hours. In addition, for case (iii) two reference measurements were taken with one right before dispensing the pill and one an hour after completion of the 8 post prandial measurements, i.e ., at hour 5, for a total of 11 readings.

The purpose of the first set of measurements was to establish the baseline for this diabetic subject: the recovery period of post-prandial blood glucose excursion without medication. The second and the third sets of the trials were designed to quantitatively measure the hypoglycemic drug effects and the most optimal time frame to administer the pills. Raw data were averaged and the corresponding standard deviations were also calculated for 5 replicates at given times. The averaged data were then used for modeling analysis.

Model formulation

The post-prandial blood glucose excursion can be considered as a hormone regulated resilient system. The food intake is treated as a bolus injection of glucose, and thus the impulse force f ( t ); effects of exercises and hypoglycemic medication are lumped as the damping factor, β . The differential equation of such an oscillatory system, that is used to describe post-prandial blood glucose excursions, can be found in many physics texts:

where x represents blood glucose level over the baseline at time t , ω 0 is the system natural frequency [ 12 ]. The pre-prandial blood glucose levels are generally fluctuating with relatively insignificant magnitudes thus can be approximated as a flat level. If the impulse force f ( t ) takes the form of the Dirac delta function, F δ ( t -0) with F being a food intake dependent parameter, the solution of Eq. (1) is

Parametric estimation

For a given blood glucose excursion, data was taken every 30 minute interval from the time a meal was initially consumed, from which the excursion peak ( MR ), x max , and the corresponding time τ to reach MR can both be estimated. Setting dx / dt = 0 in Eq. (2), the time τ can be expressed as:

Substituting Eq. (3) into Eq.(2), we have

The area under an excursion curve, AUC , can also be obtained:

where T = 2 π / ω is the period of oscillation. The reason for setting the upper integral limit to T /2 is because the damping factor β effectively depresses the glucose excursion levels x near zero for t > T /2, i.e ., it ripples about pre-prandial level. The time T /2 is therefore defined as the recovery period ( RP ). For type 2 diabetic patients who are not in a properly structured regimen, the recovery periods are often longer than 5 hours, by which time the next meal arrives and induces another blood glucose upswing.

Equations (3) – (5) can be used to estimate the three parameters, F , ω and β , from the measurable quantities of τ , x max , and AUC . The procedure is briefly described below:

1. Assign T as twice the roughly estimated recovery period in hours, which can be obtained from the raw data and thus ω = 2 π / T .

4. Fine tune these three parameters by using MATLAB function fminsearch to minimize [ AUC data - AUC ( F , β , ω )] 2 , where AUC data is calculated from the averaged data points by the trapezoidal rule and AUC ( F , β , ω ) is calculated from Eq. (5).

5. These three parameters can further be fine-tuned by fminsearch (sum of squared errors between the averaged data points and the model predicted values).

Two MATLAB user defined functions: GlucoseModel (for No pill and Pill at meal) and GlucoseModel1 (for Pill one hour prior) to estimate these model parameters and calculating the relevant diabetic characteristic measures: τ , x max , AUC are listed in the Additional files 1 and 2 , respectively.

Table ​ Table1 1 lists the fine-tuned values of model parameters: F , ω , β , and those characteristic parameters: RP , τ , x max , and AUC , the latter three are calculated from Eqs. (3) to (5). Also included in Table ​ Table1 1 are the fitting statistics R 2 values that indicate how well model curves fit the data.

Model and characteristic parameters for the post-prandial blood glucose excursion

The parametric value of F is the result of food impact, or the rate of glucose being absorbed into the blood stream. The interpretation of F is rather difficult as the liver acts as a storage compartment for glucose [ 12 ]. Liver regulates blood plasma glucose levels; if it is too high, the excess will be stored in the liver, and the reverse process will take place if the plasma glucose is too low. Although all three model parameters: F , ω , and β are more or less influenced by the liver function, the impact on F deems more pronounced as it has a direct impact on the glucose levels in the blood stream. As the function of the liver is not included in the current model, the estimated F values can only be loosely inferred as a function of insulin level, F increases as hypoglycemic drug depresses the blood glucose levels that in turn increases the absorption rate of glucose into the blood stream as in the case of 1/4 pill taken right before the meal. When the drug is taken an hour before the meal, the liver may have sufficient time to regulate blood glucose levels that additional glucose absorption becomes less intensive.

Ratio of characteristic parameters for the post-prandial blood glucose excursion

No pill trial

Parametric values for no-pill trial reveal that glucose absorption rate is generally slower (low F value) in comparison with the other two cases. The exceedingly long RP of nearly 7 hours is undesirable: as it implies that the next meal time arrives before the blood glucose level could return to the baseline, i.e ., an elevated blood glucose level would be sustained for a prolonged period of time. The high RP and AUC are unmistakably the characteristics for type 2 diabetes. Figure ​ Figure1 1 compares the model and the data with the corresponding standard deviation bars. Model curves are extended for an additional hour beyond the last data point (and in all the figures herewith) to denote the trend of blood glucose excursion.

Post-prandial glucose excursion: no pill trial

1/4 of 5 mg glyburide taken right before the meal

The blood glucose characteristics are significantly improved with a 1/4 size of 5 mg glyburide taken right before lunch. Increased ω and β values translate to significantly lower RP and AUC with virtually unchanged x max . Although the mean RP is less than 5 hours, it is still a bit too long in comparison with the non-diabetics [ 12 ] (~ 4 hours). A higher F value than the one for no-pill trial may partly due to the liver intervention. Figure ​ Figure2 2 compares the model and the data. From the figure one can tell that hypoglycemic drug has an effective delayed effect of about two hours as the rising portion of the model is almost identical to the one for no-pill trial with both x max are about 60, which may be the result of liver function that with initial stimulation of hypoglycemic drug, liver may also release glucose. As the hypoglycemic drug effect persists, the liver ceases to interfere.

Post-prandial glucose excursion: 1/4 pill right before the meal

1/4 of 5 mg glyburide taken an hour before the meal

From the personal experience of the participating subject, the hypoglycemia usually occurs 3 to 4 hours after taking the pill. The trial described in the previous section also reveals that no significant hypoglycemic drug effect is detected in the initial two hours. In order to learn the drug impact on an empty stomach, an additional glucose measurement was made prior to taking the hypoglycemic pill at -1 hour. Another measurement was also taken an hour after the blood glucose excursion returned to the baseline ( i.e ., at hour 5). This is meant to check if the blood glucose would remain near the baseline level. The drop of blood glucose levels between -1 and 0 hours are roughly 10 mg/dL, which can be contributed to the mild liver intervention. No net hypoglycemic drug effect is taking place before the meal as evidenced from the initial rise of the blood excursion curve as shown in Fig. ​ Fig.3 3 (in comparison with Fig. ​ Fig.2), 2 ), where only data between hour 0 and hour 4 were used to generate the model curve. Indeed, all parametric values are improved significantly: both PR and x max are decreased by 20% and their combination that reflected in AUC dropped nearly 35% in comparison to those for pill taken at meal trial as shown in Table ​ Table1. 1 . The food impact parameter F decreased a little from the one for pill at meal trial, which may indicate an hour after dispensing the pill, a quasi-equilibrium state has been reached among the liver function, hypoglycemic drug effects, and the bolus injection of glucose. The system frequency ω increased for more than 25%, which gives a shorter RP that compares favorably with non-diabetics. The drop of damping factor β may be the result of low F , as both τ and x max are already significantly reduced that further strengthening of β becomes unnecessary. The hour 5 measurements confirm that although the model curve shows a decreasing trend, upon returning to the base level the blood glucose excursions practically stabilizes. In addition, the volunteer patient did not experience any hypoglycemia even two to three hours after the final post-prandial measurement.

Post-prandial glucose excursion: 1/4 pill an hour before the meal

This simple impulse-forced model provides a means to shape a self-management regimen for the type 2 diabetic subject: a moderate meal coupled with minimal amount of medical intervention has effectively modulated the blood glucose excursion by reducing its recovery periods and fluctuation amplitudes. Based on the model, the type 2 diabetic subject was able to adjust a lifestyle that include (a) 40 minute swimming in a 25 m pool in the morning, (b) a fruit of mid-size apple or its equivalent and a cup of coffee with cream for breakfast without taking hypoglycaemic pill, (c) moderate lunch with 1/4 size of 5 mg glyburide taken 1/2 to 1 hour before the meal, (d) moderate early dinner, 4 hours prior to bed time, with 1/4 size of 5 mg glyburide taken 1/2 to 1 hour before the meal, (e) snack a mid-size banana, or a small bag (3.5 oz) of peanuts, or 6 crackers when needed in between meals. With this regimen, he was able to reduce his A1c level from 6.7 to 6.0 in 6 months and maintained at this level for the subsequent 6 months. Moreover, he has not had any hypoglycaemic bouts ever since he particitipated in this study more than two years ago.

Elevated blood glucose excursions during the night would boost the A1c levels. To keep a low average fluctuation of blood glucose excursion amplitudes, the evening meal is crucial. In order to avoid hypoglycaemia during the sleep, an early dinner is advised. The subject has been able to keep post-prandial blood glucose levels within 200 mg/dL with the mean fasting reading of 90 ± 20 mg/dL. Occasionally he consumes a can of beer or sugar free deserts. Although no rigorous study has been performed, a forty-minute exercise of swimming, or weight lifting, or jogging, or any combination of these is roughly equivalent to the effect of 1/4 size of 5 mg glyburide. Nonetheless, it is impractical to exercise more than once a day, thus the subject takes 2.5 mg of hypoglycemic pill a day instead. His physician originally prescribed him to take one 5 mg hypoglycemic pill daily. That was more that 10 years ago. The regimen did not work very well as he experienced hypoglycaemic bouts often. This model-based regimen not only reduced A1c level but entirely eliminated hypoglycaemic symptoms. In addition, one fasting blood glucose measurement in the morning is sufficient for him to maintain a healthy daily routine of exercise, consuming meals/snacks and leading a productive life with mental and physical activities.

Lifestyle adjustments are the best regimens for many chronicle ailments such as diabetes, hypertension, high cholesterol levels, etc . Although this model-based self-management regimen for the type 2 diabetic subject is only a case study, it certainly provides a general guideline for an applicable life-style adjustment. Currently not all the model parameters are entirely clear, additional data are required to draw a meaningful general conclusion. A pilot project of testing this regimen on six type 2 diabetic patients in a regional nursing home is proposed for the next phase of study.

Although derived characteristic parameters: RP and AUC (to a lesser degree, τ and x max ), carry clear meaning that can be used to characterize type 2 diabetic subjects from non-diabetics, the implications of model parameters, F , ω and β are not as translucent. With additional data, one may be able to draw plausible conclusions about (a) how F is influenced by food intakes, drug (delaying) effects, and liver (regulatory) functions; and (b) how ω and β behave, whether they are independent of F and of each other, or all three somewhat mutually dependent. Better understanding of these parameters would definitely enhance the self-management for type 2 diabetes.

This model-based lifestyle adjustment has another advantage: it can be used to manage each individual needs. Nutall and Chasuk [ 10 ] have stressed that dietary recommendation for type 2 diabetes should be flexible and highly individualized; most of prepared meal programs and exchange-list diets for diabetes have not had individualization in mind nor are they designed for ethnic minorities. Once we have a comprehensive understanding of these parameters, it is possible to tailor individual lifestyle adjustment accordingly.

For those individuals who are interested in self-managing the type 2 diabetes, the general advice is: avoiding big meals, may snack moderately between meals, eat an early dinner – about 4 hours before bedtime, and exercise regularly. If one is interested in "normal" meal effects on one's post-prandial blood glucose excursion, taking a pre-prandial blood glucose measurement prior to a typical lunch and 8 to 10 post-prandial measurements at half-hour intervals for 5 or more replicates and follow the procedure described here to obtain these characteristic parameters RP , τ , x max , and AUC . Applying a small dosage of medical intervention prior to a meal can keep the blood glucose at a relatively flat level and depress the overnight blood glucose excursion; however, this practice needs the approval from one's family physician and is not recommended here.

Authors' contributions

Sole authorship: data collection/analysis, model building, parameter estimation/interpretation, and the design of life-style adjustment regimen for the participating subject.

Supplementary Material

MATLAB user defined function: GlucoseModel (for No pill and Pill at meal) to estimate model parameters: F , β , ω and to calculate the relevant diabetic characteristic measures: τ , x max , AUC .

MATLAB user defined function: GlucoseModel1 (for Pill one-hour prior) to estimate model parameters: F , β , ω and to calculate the relevant diabetic characteristic measures: τ , x max , AUC .

Acknowledgements

The author wishes to express his appreciation to Ms. Katherine Jakubik for her editing efforts, to Professor Jame B. Bassingthwaighte and two other anonymous reviewers for their critical comments to an earlier version of this manuscript.

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• Open access
• Published: 17 January 2024

Nutrient patterns and risk of diabetes mellitus type 2: a case-control study

• Morteza haramshahi 1 ,
• Thoraya Mohamed Elhassan A-Elgadir 2 ,
• Hamid Mahmood Abdullah Daabo 3 ,
• Yahya Altinkaynak 4 ,
• Ahmed Hjazi 5 ,
• Archana Saxena 6 ,
• Mazin A.A. Najm 7 ,
• Abbas F. Almulla 8 ,
• Ali Alsaalamy 9 &
• Mohammad Amin Kashani 10  

BMC Endocrine Disorders volume  24 , Article number:  10 ( 2024 ) Cite this article

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Backgrounds

Although the significance of diet in preventing or managing diabetes complications is highlighted in current literature, there is insufficient evidence regarding the correlation between nutrient patterns and these complications. The objective of this case-control study is to investigate this relationship by analyzing the dietary intake of nutrients in participants with and without type 2 diabetes (T2D).

A case-control study was conducted at the Tabriz Center of Metabolism and Endocrinology to investigate the relationship between nutrient patterns and type 2 diabetes (T2D). The study enrolled 225 newly diagnosed cases of T2D and 225 controls. The dietary intake of nutrients was assessed using a validated semi-quantitative food frequency questionnaire (FFQ). Principal component analysis using Varimax rotation was used to obtain nutrient patterns. Logistic regression analysis was performed to estimate the risk of T2D.

The participants’ mean (SD) age and BMI were 39.8 (8.8) years and 27.8 (3.6) kg/m2, respectively. The results identified three major nutrient patterns. The first nutrient pattern was characterized by high consumption of sucrose, animal protein, vitamin E, vitamin B1, vitamin B12, calcium, phosphorus, zinc, and potassium. The second nutrient pattern included fiber, plant protein, vitamin D, Riboflavin, Vitamin B5, copper, and Magnesium. The third nutrient pattern was characterized by fiber, plant protein, vitamin A, riboflavin, vitamin C, calcium, and potassium. Individuals in the highest tertile of nutrient pattern 3 (NP3) had a lower risk of T2D compared to those in the lowest tertile after adjusting for confounders. The odds ratio was 0.52 with a 95% confidence interval of 0.30–0.89 and a P_trend of 0.039.

This study found that conforming to a nutrient pattern consisting of plant protein, vitamin C, vitamin A, vitamin B2, potassium, and calcium is linked to a lower likelihood of developing T2D.The initial results suggest that following a nutrient pattern that includes these nutrients may reduce the risk of T2D. However, further research is required to confirm the relationship between nutrient patterns and T2D.

Peer Review reports

Type 2 diabetes is a significant concern for public health in developed nations. It leads to high rates of illness and death and places a significant financial burden on healthcare systems [ 1 , 2 ]. In the past few decades, there has been a sharp increase in the occurrence of diabetes, and is expected to continue increasing, with an estimated 693 million people living with the disease by 2045 [ 1 ]. Complications associated with type 2 diabetes can also contribute to premature death. A concerning aspect of the disease is that a significant proportion of cases (40%) go undetected [ 3 ], and there is also an increasing prevalence of prediabetes, which raises the risk of developing type 2 diabetes and other chronic diseases [ 1 ].

The connection between diet and type 2 diabetes has been extensively studied, including the examination of dietary patterns and individual foods or nutrient patterns [ 4 , 5 , 6 , 7 ]. Various sources have suggested that chronic diseases may be influenced by a combination of nutrients [ 8 ]. In the field of nutritional epidemiology, the examination of dietary patterns has emerged as a viable approach to investigate the correlation between diet and disease. This method involves using statistical techniques to combine multiple foods or nutrients into dietary or nutrient patterns, which are believed to provide a more detailed understanding of the connection between diet and disease. It has been suggested that the impact of individual nutrients or foods on chronic disease may be too subtle to detect, but their collective effect within a pattern may be more indicative [ 9 ].

There have been some recent studies examining the effect of nutrient patterns on chronic disease such as, non-alcoholic fatty liver, breast and gastric cancer, Polycystic Ovary Syndrome (PCOs) and metabolic syndrome [ 10 , 11 , 12 , 13 , 14 ]. For example, it was found that a nutrient pattern consisting mainly of protein, carbohydrates, and various sugars was linked to a higher risk of Metabolic Syndrome (MetS) in both men and women, whereas a pattern characterized by copper, selenium, and several vitamins was linked to greater odds of MetS [ 14 ]. A prospective study conducted among participants of the Tehran Lipid and Glucose Study indicates that a nutrient pattern rich in vitamin A, vitamin C, vitamin B6, potassium, and fructose is associated with a reduced risk of insulin-related disorders [ 15 ]. Although there have been limited investigations on the connection between nutrient patterns and the likelihood of developing diabetes, the present study seeks to explore this relationship by analyzing the adherence to different nutrient patterns and its effect on the risk of type 2 diabetes.

Study population

This study utilized a case-control design and involved participants between the ages of 18 and 60 who had been diagnosed with type 2 diabetes within the previous six months based on specific glucose level criteria (FBS levels of ≥ 126 mg/dl and 2 h-PG levels of ≥ 200 mg/dl [ 17 ]). Healthy individuals within the same age range were also included, with specific glucose level criteria (FBS levels of < 100 mg/dl and 2 h-PG levels of < 200 mg/dl [ 17 ]). The study excluded individuals with certain chronic diseases, Type 1 Diabetes, gestational diabetes, those following specific dietary patterns or taking certain medications, pregnant and breastfeeding women, those with a family history of diabetes or hypertension, and those who did not complete the food frequency questionnaire (more than 35 items) or whose reported energy intake was outside of a specific range (range of 800–4200 kcal [ 18 ]).

This study enrolled 450 adult participants, with 225 individuals in the case group and 225 in the control group. The case group was selected using a simple sampling method from patients diagnosed with diabetes at the Tabriz Center of Metabolism and Endocrinology as a referral center affiliated to tabriz University of Medical Sciences from January 2021 to March 2022, as well as through a two-stage cluster sampling method among patients referred to private endocrinologists to enhance the sample’s external validity. Participants in the control group were also selected through a two-stage cluster sampling method from individuals who had undergone blood glucose checkups at the Tabriz Center of Metabolism and Endocrinology, a referral center affiliated with Tabriz University of Medical Sciences, within the past six months. All participants provided informed consent at the beginning of the study. The study was financially supported by Tabriz University of Medical Sciences and is related to project NO. 1400/63,145.

Dietary assessment

To collect dietary intake information, personal interviews and a semi-quantitative food frequency questionnaire (FFQ) consisting of 168 food items were used [ 16 ]. The FFQ asked about the frequency of consumption for each item over the course of one year, with the year before diagnosis for the case group and the year before the interview for the control group. Participants were also asked about the frequency of consumption (per day, week, month, or year) for each type of food. to ensure consistency in measurements, a nutritionist provided instructions on converting the size of reported food items from household measures to grams using four scales. The quantity of food consumed by each individual was calculated based on their intake in grams and reported on a daily basis. The nutrient composition of all foods was derived by using modified nutritionist IV software.

Nutrient pattern assessment

We conducted factor analyses using a comprehensive set of 34 nutrients, encompassing various macronutrients, micronutrients, and other dietary components. These included sucrose, lactose, fructose, fiber, animal protein, plant protein, saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, cholesterol, as well as an array of vitamins and minerals such as A, D, E, K, C, thiamine (B1), riboflavin (B2), niacin (B3), pantothenic acid (B5), pyridoxine (B6), folate (B9), B12, calcium, phosphorus, iron, zinc, copper, magnesium, manganese, chromium, selenium, sodium, potassium, and caffeine. The dietary intake of these 34 nutrients per 1,000 Kcal of energy intake was computed and utilized as input variables. Subsequently, nutrient patterns (NPs) were derived through principal component analysis (PCA) with varimax rotation, based on the correlation matrix. Factor scores for each participant were then calculated by aggregating the frequency of consumption and multiplying it by the factor loadings across all 34 nutrients. To assess the statistical correlation between variables and evaluate the adequacy of the sample size, we employed the Bartlett test of sphericity ( P  < 0.001) and the Kaiser-Mayer-Olkin test (0.71), respectively.

Assessment of other variables

To obtain the participants’ anthropometric measurements, weight and height were measured using a seca scale, and the participants’ BMI was determined by dividing their weight in kilograms by the square of their height in meters. Waist circumference was measured using a metal anthropometric tape, and the participants’ hip circumference was measured using a metal anthropometric tape while standing [ 17 ]. Daily physical activity was measured using a physical activity questionnaire [ 18 ], and personal questioning was employed to gather information on population and socioeconomic characteristics, including marital status, academic degree, and smoking.

Statistical analysis

Statistical analysis was performed using the Statistical Package Software for Social Science, version 21. The normality of the data was assessed using Kolmogorov-Smirnov’s test and histogram chart. The characteristics and dietary intakes of the case and control groups were presented as mean ± SD or median and frequency (percentages). Independent sample t-tests and chi-square tests were used to compare continuous and categorical variables, respectively, between the case and control groups.

The participants’ mean (SD) age and BMI were 39.8 (8.8) years and 27.8 (3.6) kg/m2, respectively. The mean (SD) BMI in the case group was 30.5 ± 4.1, and in the control group, it was 25.2 ± 3.2 kg/m2. The mean (SD) physical activity in the case group was 1121 ± 611 MET/min/week, and in the control group, it was 1598 ± 940 MET/min/week. There were significant differences in BMI and physical activity between the two groups. The mean (SD) waist circumference in the case group was 109.32 ± 10.28 cm, and in the control group, it was 87.25 ± 9.35 cm. The mean (SD) hip circumference in the case group was 107.25 ± 8.61 cm, and in the control group, it was 91.44 ± 6.17 cm. The study identified three primary nutrient patterns (NPs) with eigenvalues greater than 2. Table  1 displays the factor loadings for nutrient patterns, which accounted for 56.11% of the total nutrient variation. The high intake of sucrose, animal protein, phosphorus, zinc, potassium, calcium, vitamin E, vitamin B1 and vitamin B12 were the distinguishing features of the first pattern. The second nutrient pattern was positively associated with copper, magnesium, fiber, vitamin D, B2, B5 and plant protein but had a negative correlation with lactose and saturated fatty acids. On the other hand, the high intake of fiber, vitamin A, B2, vitamin C, plant protein and potassium were the distinguishing features of the third pattern.

The following are the characteristics of T2D patients compared to the control group, as shown in Table  2 : Higher BMI, More likely to be smokers, Lower physical activity levels, higher FBS, HbA1C, Insulin ( p  < 0.05). Other variables did not differ significantly between the two groups ( p  > 0.05). Additionally, T2D patients had a greater intake of energy and vitamin B3 but consumed less plant protein, vitamin A, vitamin E, vitamin B2, and zinc ( p  < 0.05).

Table  3 summarizes the partial correlation coefficient between NPs and food sources, with NP1 showing a strong positive correlation with low-fat dairy, NP2 with refined grains, and NP3 with fruits and vegetables.

Table  4 demonstrates the relationships between NPs and T2D. After adjusting for age and sex, there was no significant link between each nutrient pattern (NP) and T2D. However, when adjusting for other factors such as BMI, physical activity, smoking, and energy intake, individuals in the highest tertile of NP1 and NP2 did not show a significant association with T2D compared to those in the lowest tertile. On the other hand, those in the highest tertile of NP3 had a lower probability of developing T2D than those in the lowest tertile (OR: 0.52, 95%CI: 0.30–0.89, P_trend = 0.039).

In this study, three major NPs were identified. After adjusting for potential confounders, we observed a significant inverse association between the Third NP and the odds of T2D. The high intake of fiber, vitamin A, B2, vitamin C, plant protein and potassium were the distinguishing features of the third pattern.

Dietary patterns, such as healthy, Mediterranean, traditional, and Western dietary patterns, have recently received significant attention in studying the connection between diet and health. When looking at the relationship between nutrients and disease incidence, it is more challenging to evaluate when considering individual foods and the metabolism of all nutrients together [ 19 ]. It is therefore more effective to take a broader view and consider diet as a whole. Dietary and nutrient patterns can have a greater impact on health than specific nutrients or nutritional groups. There is supporting evidence that links high calorie or high glycemic index foods with an increased risk of T2D. The quality of one’s diet is also associated with the risk, progression, and side effects of T2D [ 20 ]. Establishing a desirable food pattern has become a priority in public health efforts to prevent T2D. By studying dietary and nutrient patterns, we can gain a comprehensive understanding of an individual’s overall diet beyond just the consumption of specific nutrients and food groups. Moreover, it is easier for people to understand health recommendations when presented as dietary patterns rather than focusing solely on individual nutrients [ 19 ].

A previous cross-sectional study investigated the relationship between NPs and fasting glucose and glycated hemoglobin levels among apparently healthy black South Africans. The study stratified 2,010 participants by gender and urban/rural status and identified three nutrient patterns per stratum. In rural women, a nutrient pattern driven by starch, dietary fiber, and B vitamins was significantly associated with lower fasting glucose and glycated hemoglobin levels. A nutrient pattern that included vitamin B1, zinc, and plant protein was linked to notable decreases in glycated hemoglobin and fasting glucose levels in rural men. These findings suggest that nutrient patterns that are plant-based are linked to lower levels of fasting glucose and glycated hemoglobin [ 21 ].

Iwasaki et al. found that specific nutrient patterns were associated with lower risks of MetS. One nutrient pattern high in potassium, fiber, and vitamins, while another pattern high in vitamin B2, saturated fatty acids and calcium [ 22 ]. A recent study found that a nutrient pattern characterized by high intake of calcium, potassium, fats, cholesterol, vitamins B2, B12, A, D, K and C was positively linked to MetS [ 23 ]. Salehi-Sahlabadi et al. found that adhering to a nutrient pattern rich in potassium, vitamin A, fructose, vitamin C and vitamin B6 was negatively associated with the likelihood of NAFLD [ 11 ]. A nutrient pattern high in potassium, vitamin A, vitamin B6, vitamin C and fructose was associated with a reduced risk of hyperinsulinemia, IR, and dyslipidemia among participants in Tehran, according to a prospective study [ 11 , 24 , 25 ].

Due to several variations among studies exploring NPs linked to chronic diseases, including differences in the number of nutrients, populations, study designs and outcomes there has been a considerable diversity in the identified NPs, with only a few NPs being replicated across studies. Our study is the first of its kind to explore the correlation between nutrient patterns and T2D in this context.

In our study, there was no association between NPs 1 and 2 and T2D. This lack of correlation may be attributed to the absence of harmful nutrients or food categories linked to diabetes in these NPs. NP3 in this study, unlike other NPs, is positively associated with beneficial food groups such as nuts, fruits, plant oil and vegetables, and negatively associated with unhealthy food groups like red-processed meat, snacks, high-fat dairy and refined grains. A recent systematic review and meta-analysis found that individuals who consumed higher amounts of fruits and vegetables had a lower risk of developing type 2 diabetes [ 26 ]. Moreover, the consumption of vegetables was found to have an inverse relationship with ALT, TC and LDL levels among adults, while fruit consumption was associated with a positive reduction in visceral fat [ 27 , 28 ]. Another study suggested that an increased intake of vegetables and fruits could potentially lower the risk of MetS [ 29 ]. According to a study, greater nut consumption was significantly linked to a reduced prevalence of T2D [ 30 ]. Consuming fruits and vegetables is a crucial component of a healthful dietary pattern that can lower the risk of type 2 diabetes [ 31 ]. On the other hand, Consuming a Western dietary pattern, which primarily consists of fast foods, high-fat dairy, refined grains, soft drinks and processed meat has been found to be correlated with an increased risk of type 2 diabetes [ 31 ].

Several mechanisms have been identified that explain the positive associations between the components of NP 3 and T2D or its risk factors. Vitamin intake has been shown to play a role in the development of T2D through various pathways. Consuming vitamin C has been found to have beneficial effects in reducing the risk of type 2 diabetes mellitus. These effects can be attributed to the following actions of vitamin C: vasodilator, cytoprotective, platelet anti-aggregator and anti-mutagenic. To achieve this, the body increases the production of several substances including prostaglandin E1, PGI2, endothelial nitric oxide, and lipoxin A4. Additionally, the body restores the Arachidonic Acid content to normal levels [ 32 ]. Vitamin A has a multifaceted role in cell regulation beyond its antioxidant function. It contributes to gene regulation, epithelial cell integrity, and resistance to infection. Research suggests that vitamin A also enhances antioxidant enzyme function in the body. Research has indicated a link between vitamin A deficiency and type 2 diabetes mellitus (T2DM), which suggests that vitamin A may have a role in the biology of T2DM [ 33 ]. Moreover, a meta-analysis has found that replacing animal protein with plant protein can lead to minor improvements in glycemic control for individuals with diabetes [ 34 ]. According to a recent meta-analysis, increasing the consumption of fruits, especially berries, yellow vegetables, cruciferous vegetables, green leafy vegetables is associated with a lower risk of developing type 2 diabetes. These results support the recommendation to incorporate more fruits and vegetables into the diet as a way to prevent various chronic diseases, including type 2 diabetes [ 35 ]. A study showed that maintaining adequate potassium intake could regulate insulin secretion and carbohydrate metabolism, leading to the prevention of obesity and metabolic syndrome (MetS) [ 36 ].

A number of research studies conducted in the Western societies have shown that Western dietary pattern including higher intake of red meat, processed meat, and refined grains is significantly associated with increased risk of T2D [ 37 , 38 ]. For example, in the 12-years cohort prospective study, van Dam et al. investigated dietary pattern of 42,504 American white men at the age range of 40–75 years old using the FFQ. After controlling the confounders, the risk of T2D increased 60% in people adherent to the western-like dietary pattern [ 38 ]. The rapid process of change in lifestyle, diets, and physical activity that have been occurred as a result of extended urbanization, improved economic status, change of work pattern toward jobs, and change in the processes of producing and distributing nutrients during the recent years in developing countries have led people to more consumption of fast food and processed foods [ 20 ].

Significant research has been conducted on the impact of nutrient type and sequence on glucose tolerance. Multiple studies have shown that manipulating the sequence of food intake can enhance glycemic control in individuals with type 2 diabetes in real-life situations. The glucose-lowering effect of preload-based nutritional strategies has been found to be more pronounced in type 2 diabetes patients compared to healthy individuals. Moreover, consuming carbohydrates last, as part of meal patterns, has been proven to improve glucose tolerance and reduce the risk of weight gain [ 39 ]. Recent findings on meal sequence further emphasize the potential of this dietary approach in preventing and managing type 2 diabetes [ 40 ].

Several studies have shown that food from a short supply chain has a significant impact on metabolic syndrome. The length of the food supply chain is important in determining the risk of metabolic syndrome in a population [ 41 ]. Research indicates that people who consume food from short supply chains have a lower prevalence of metabolic syndrome compared to those who consume food from long supply chains. Specifically, food from short supply chains is associated with lower levels of triglycerides and glucose, which leads to a reduced occurrence of metabolic syndrome [ 42 ]. Adhering to the Mediterranean diet with a short supply chain is also found to significantly reduce the prevalence of metabolic syndrome. Therefore, these studies provide evidence that food from short supply chains positively affects metabolic parameters and the occurrence of metabolic syndrome [ 41 ].

The study we conducted presented several advantages. It was the first case-control research to investigate the correlation between nutrient patterns and the likelihood of developing type 2 diabetes (T2D). While numerous studies have explored the relationship between dietary patterns and diabetes, there is a scarcity of research specifically focusing on nutrient patterns in individuals with type 2 diabetes. Furthermore, the collection of dietary intake data was carried out through face-to-face interviews conducted by trained dieticians to minimize measurement errors. However, this study also had some limitations. Case-control studies are susceptible to selection and recall biases. Additionally, the use of factor analysis to identify patterns, and the potential influence of research decisions on the number of factors and nutrient factor loadings in each pattern, should be considered. Lastly, despite the use of a validated semi-quantitative FFQ (food frequency questionnaire), there remains a possibility of measurement error due to dietary recall. The study’s findings and limitations contribute to the ongoing discourse on the role of nutrient patterns in the development of T2D and the importance of considering these factors in future research and preventive strategies.

Conclusions

The results of this study indicate that conforming to a nutrient pattern consisting of plant protein, vitamin C, vitamin A, vitamin B2, potassium, and calcium is linked to a lower likelihood of developing T2D. Our investigation did not reveal any significant correlation between other nutrient patterns and T2D risk. However, additional research is necessary to authenticate these initial findings and establish the correlation between nutrient patterns and T2D.

Data availability

Upon reasonable request, the corresponding author can provide the datasets that were produced and analyzed during the current study.

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The researchers express their gratitude towards all the individuals who volunteered to take part in the study.

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Morteza haramshahi

Department of clinical biochemistry, College of medicine, King Khalid University, Abha, Saudi Arabia

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Contributions

The study’s protocol was designed by M.K., M.H., and T.E., while H.A., Y.A., and A.H. carried out the research. A.S. analyzed the data and prepared the initial draft of the manuscript. M.N., A.FA., and A.A. interpreted the data and provided critical feedback on the manuscript. All authors reviewed and approved the final version of the manuscript.

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Correspondence to Mohammad Amin Kashani .

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haramshahi, M., A-Elgadir, T.M.E., Daabo, H.M.A. et al. Nutrient patterns and risk of diabetes mellitus type 2: a case-control study. BMC Endocr Disord 24 , 10 (2024). https://doi.org/10.1186/s12902-024-01540-5

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DOI : https://doi.org/10.1186/s12902-024-01540-5

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Kidney and CV Effectiveness of SGLT2 Inhibitors vs. GLP-1 RAs in Diabetes

Quick Takes

• This multicenter study reports SGLT2i and GLP-1 RAs exert many overlapping kidney and CV effects in people with T2D, although SGLT2i may lead to more improvements in eGFR.
• Given the observational nature of this study, additional clinical trials are indicated to directly compare SGLT2i and GLP-1 RAs in people with and without T2D over longer follow-up durations.
• The large amount of overlapping cardiorenal benefits from these medications in this study suggests that a combination of SGLT2i and GLP-1 RA therapy may be a consideration.

Study Questions:

What are the kidney and cardiovascular (CV) outcomes for new users of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) among people with type 2 diabetes (T2D)?

The investigators analyzed electronic health record data from 20 US health systems contributing to PCORnet between 2015 and 2020 using propensity score overlap weighting. The primary kidney outcome was a composite of sustained 40% estimated glomerular filtration rate (eGFR) decline, incident end-stage kidney disease, or all-cause mortality over 2 years or until censoring. In addition, the authors examined CV and safety outcomes. Cox proportional hazards models were used to estimate time to first event and presented weighted incidence rates as number of first events per 1,000 patient-years of follow-up.

The weighted study cohort included 35,004 SGLT2i and 47,268 GLP-1 RA initiators. Over a median of 1.2 years, the primary outcome did not differ between treatments (hazard ratio [HR], 0.91; 95% confidence interval [CI], 0.81-1.02), although SGLT2i were associated with a lower risk of 40% eGFR decline (HR, 0.77; 95% CI, 0.65-0.91). Risks of mortality (HR, 1.08; 95% CI, 0.92-1.27), a composite of stroke, myocardial infarction, or death (HR, 1.03; 95% CI, 0.93-1.14), and heart failure hospitalization (HR, 0.95; 95% CI, 0.80-1.13) did not differ. Genital mycotic infections were more common for SGLT2i initiators, but other safety outcomes did not differ. The results were similar regardless of chronic kidney disease status.

Conclusions:

The authors report that SGLT2i and GLP-1 RAs led to similar kidney and CV outcomes in people with T2D, though SGLT2i initiation was associated with a lower risk of 40% eGFR decline.

Perspective:

This multicenter study reports SGLT2i and GLP-1 RAs exert many overlapping kidney and CV effects in people with T2D, although SGLT2i may lead to more improvements in eGFR. Given the observational nature of this study, additional clinical trials are indicated to directly compare SGLT2i and GLP-1 RAs in people with and without T2D over longer follow-up durations. The large amount of overlapping cardiorenal benefits from these medications in this study suggests that a combination of SGLT2i and GLP-1 RA therapy may be a consideration. Furthermore, addition of the mineralocorticoid receptor antagonist finerenone may further improve cardiorenal outcomes in these patients.

Clinical Topics: Diabetes and Cardiometabolic Disease, Prevention

Keywords: Diabetes Mellitus, Type 2, Kidney Diseases, Novel Agents

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Case report

• Open access
• Published: 15 August 2024

Infection of Mycoplasma hominis in the left lower leg amputation wound of a patient with diabetes: a case report

• Li-Chen Kuo 1 , 2 ,
• Yu-Hsiang Tseng 3 ,
• Lee-Wei Chen 3 ,
• Tso-Ping Wang 1 ,
• Ciao-Shan Chen 1 &
• Herng-Sheng Lee 1  

Journal of Medical Case Reports volume  18 , Article number:  380 ( 2024 ) Cite this article

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Metrics details

Mycoplasma hominis is typically found on the mucosal epithelium of the human genital tract, with infections being rare. However, when the mucosal barrier is compromised or in individuals with weakened immune systems, this microorganism can trigger infections in both intragenital and extragenital sites. This study offers a comprehensive overview of infections caused by the rare pathogen M. hominis . This overview helps laboratories identify M. hominis infections in a timely manner, thereby enabling earlier clinical intervention for patients.

Case presentation

A 75-year-old Taiwanese man with type 2 diabetes mellitus initially underwent a left lower extremity amputation following a severe infection caused by necrotizing fasciitis. Subsequently, a poorly healing wound developed at the site of amputation. Upon culturing the wound abscess, M. hominis was isolated and identified as the causative agent.

Conclusions

Through this case, we present clinical and microbiological observations along with a review of the literature to deepen our understanding of M. hominis . Our findings can be used to develop laboratory diagnostic protocols and innovative therapeutic approaches.

Peer Review reports

Introduction

Mycoplasma hominis , a commensal bacterium residing in the genital tract, lacks a conventional bacterial cell wall and is characterized by a negative Gram stain. Cultivation of M. hominis requires specific conditions, such as blood and chocolate agar plates, incubated at 37 °C with 5–10% CO 2 .The colonies are small (0.2 mm in diameter), translucent, and easily mistaken for droplets, necessitating prolonged incubation for proper development. When conventional Gram staining fails to detect microbes, suspicions of M. hominis presence arise, prompting subculture on specialized media. Various commonly used agar media, such as SP4 agar supplemented with arginine, Hayflick agar, and A7 agar, often incorporate penicillin G for enhanced selectivity. The incubation of agar plates under anaerobic conditions at 35 °C for a minimum of 5 days is recommended. Colony observation under stereomicroscopy aids identification based on their characteristic “fried egg” appearance [ 1 ]. However, reliance solely on blood or chocolate agar may not always yield reliable results, as demonstrated in our case, where bacterial growth was not detected in the second wound culture. Molecular diagnostic methods, such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI–TOF MS) or 16S rRNA sequencing, can be used to accurately identify M. hominis .

Although infections with M. hominis are rare, they predominantly affect the mucosal epithelium of the genital tract. However, compromised mucosal integrity or immune function can lead to infections in both intragenital and extragenital regions. An understanding of Mycoplasma infections requires an understanding of the effects of various factors, such as glycemic index, mineral balance, and oxidative stress; the evolutionary pathways of intracellular pathogens; and the interaction of these pathogens with our immune response—in addition to the genetic bases underlying these mechanisms. These factors play a critical role in the selection of pathogen populations over time, both locally and systemically, during the course of infection. Although M. hominis is commonly found in the urinary tract of humans, reports of this rare pathogen in diabetes-related foot infections are scarce in the English language literature, possibly due to cross-contamination. This study aimed to provide a comprehensive description of infections caused by M. hominis to assist laboratories in timely identification, which is crucial for early and effective treatment.

We present the case of a 75-year-old Taiwanese man with a medical history of end-stage renal disease undergoing hemodialysis via a left arm arteriovenous fistula. He also presented with hypertension, hyperlipidemia, and type II diabetes mellitus (DM) with retinopathy as comorbidities. Due to painful, erythematous changes and a nonhealing wound over left ankle persisting for 3 months, he sought assistance in our emergency room. His vital signs were within normal limits, with a blood pressure of 114/54 mmHg, temperature of 37.3 °C, a pulse of 71 beats per minute, and respiratory rate of 16 per minute. A physical examination revealed a 2-cm chronic wound with turbid, orange discharge over the left leg and necrotic tissue over the left ankle (Fig.  1 a). Laboratory tests revealed a white blood cell count of 7080/μL, with differential counts showing neutrophils at 38%, lymphocytes at 23%, monocytes at 18%, eosinophils at 12%, and basophils at 3%. His C-reactive protein level was elevated to 10.2 mg/dL. A computed tomography scan of the left lower extremity revealed gas formation within the subcutaneous and intermuscular fascial planes over the foot and leg, raising suspicion of necrotizing fasciitis. Consequently, empirical antibiotic therapy with piperacillin/tazobactam was initiated, and the patient was admitted for further management. Subsequent fasciectomy revealed necrosis of the ankle joint capsule, joint pus formation, necrosis of the plantar and dorsal foot periosteum (Fig.  1 b), and necrosis of the lateral compartment muscles of the lower leg (Fig.  1 c). Wound culture led to the identification of Proteus mirabilis and Peptostreptococcus anaerobius infections, prompting a change in antibiotic therapy to cefoxitin based on sensitivity testing. However, the wound emitted purulent discharge and a foul odor, and poor circulation was noted in the left lower extremity. Therefore, below-knee amputation was performed. Although initial wound cultures exhibited no bacterial growth, purulent discharge persisted from the medial part of the amputated stump 3 days later, prompting a third wound culture. Given the negative result of the second culture, the possibility of a rare pathogen was considered, leading to incubation of the third wound culture on anaerobic blood agar at 35 °C under 5% CO 2 for 5 days, which revealed pinpoint-sized colonies resembling water droplets (Fig.  1 d). These colonies tested negative on a gram stain, but MALDI–TOF MS findings led to the identification of these colonies as M. hominis . Genetic sequencing confirmed this identification (Fig.  1 e). Accordingly, antibiotic therapy was changed to intravenous levofloxacin, which resulted in resolution of purulent discharge and closure of the wound. The patient was then discharged without complications. The clinical treatment process is detailed in the supplementary file.

a A chronic wound , 2 cm in size (arrow). b Operative finding showed ankle joint capsule necrosis (1), joint pus formation (2), and plantar and dorsal foot periosteum necrosis (3). c Lateral compartment of lower leg part muscle necrosis (arrow). d Formation of pinpoint-sized colonies resembling water droplets on agar. e Result of gene sequencing performed by Genomics Bioscience and Tech. Co., Ltd. The solid blue dots (M1110221-140) in the phylogram tree belong to the Mycoplasma hominis group in our case

M. hominis is difficult to culture in microbiology laboratories due to its slow growth, which may lead to human error. In a case initially managed with piperacillin/tazobactam, the antibiotic regimen was changed to cefoxitin following the detection of Proteus mirabilis and Peptostreptococcus anaerobius in wound cultures. Despite this adjustment, the wound infection persisted until M. hominis was isolated, emphasizing delayed treatment potentially attributable to concurrent bacterial infections. Subsequent treatment with levofloxacin led to a resolution of the infection.

Infections caused by M. hominis can occur through both intragenital and extragenital routes. In genital tract infections, transmission to newborns during birth is a concern [ 2 ]. Extragenital infections can manifest in various forms, such as septic arthritis [ 3 ], prosthetic joint infections [ 4 ], central nervous system infections [ 5 ], infective endocarditis [ 6 ], abscess formation, secondary infections in joint replacement surgery [ 7 ], and wound infections [ 8 ]. Catheterization has also been associated with M. hominis , potentially due to the use of indwelling catheters used during surgery or hematogenous transmission to surgical sites [ 9 ]. However, identifying the source of infection in such cases can often be challenging.

Poorly controlled glycemia in patients with diabetes poses a risk of M. hominis infection, potentially affecting wound healing processes. The elusive nature of the bacterium prompts suspicion of infection when wound repair is suboptimal and conventional abscess cultures yield negative results. Detecting M. hominis necessitates specialized culture conditions, including 5–10% CO 2 or anaerobic conditions at 35 °C for several days. Molecular techniques such as MALDI–TOF MS and gene sequencing aid in its identification.

Recent research has uncovered the influence of various factors on immune function and cellular permeability, thereby affecting homeostasis. These factors include Glutathione (GSH)/Glutathione disulfide (GSSG) [ 10 ], vitamin E, magnesium [ 11 ], and cellular ion concentrations [ 12 ]. Additionally, cellular aging and cycling can affect stress-tolerant cell subpopulations, immune function, infection risk, and antibiotic resistance [ 13 ]. Phenotypic heterogeneity in immune cells among individuals with diabetes may contribute to increased susceptibility to infections [ 14 ]. Antioxidants such as glutathione and plasma copper levels may play a pivotal role in immune modulation [ 15 ], particularly in patients with diabetes.

The literature provides therapeutic insights for fungal infections [ 16 ] and suggests innovative strategies for treating Naegleria and intracellular pathogens, including M. hominis infections, particularly in immune-compromised patients with diabetes, autoimmune diseases, different types of neoplasia, and neurodegenerative diseases. These insights offer the potential for complete cure. These theoretical perspectives can be used to revolutionize future research approaches and deepen our understanding of immune modulation along with regeneration pathways, thereby informing treatment strategies for M. hominis infections and a wide range of other illnesses.

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Acknowledgements

This manuscript was edited by Wallace Academic Editing.

This research is provided by Kaohsiung Veterans General Hospital (KSVGH-113-085).

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Li-Chen Kuo, Tso-Ping Wang, Ciao-Shan Chen & Herng-Sheng Lee

Anatomical Pathology Department, Far Eastern Memorial Hospital, New Taipei City, Taiwan

Li-Chen Kuo

Department of Plastic Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan

Yu-Hsiang Tseng & Lee-Wei Chen

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Contributions

The investigation was carried out by Li-Chen Kuo, Yu-Hsiang Tseng, and Lee-Wei Chen. Writing—original draft preparation was performed by Li-Chen Kuo, Tso-Ping Wang, and Ciao-Shan Chen. Writing, review and editing, was performed by Li-Chen Kuo and Herng-Sheng Lee. Supervision was provided by Herng-Sheng Lee.

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Correspondence to Herng-Sheng Lee .

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Kuo, LC., Tseng, YH., Chen, LW. et al. Infection of Mycoplasma hominis in the left lower leg amputation wound of a patient with diabetes: a case report. J Med Case Reports 18 , 380 (2024). https://doi.org/10.1186/s13256-024-04718-6

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Received : 29 June 2023

Accepted : 07 June 2024

Published : 15 August 2024

DOI : https://doi.org/10.1186/s13256-024-04718-6

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The impact of PM 2.5 and its constituents on gestational diabetes mellitus: a retrospective cohort study

• Weiqi Liu 1   na1 ,
• Haidong Zou 2   na1 ,
• Weiling Liu 3   na1 &
• Jiangxia Qin 2  

BMC Public Health volume  24 , Article number:  2249 ( 2024 ) Cite this article

Metrics details

There is increasing evidence that exposure to PM 2.5 and its constituents is associated with an increased risk of gestational diabetes mellitus (GDM), but studies on the relationship between exposure to PM 2.5 constituents and the risk of GDM are still limited.

A total of 17,855 pregnant women in Guangzhou were recruited for this retrospective cohort study, and the time-varying average concentration method was used to estimate individual exposure to PM 2.5 and its constituents during pregnancy. Logistic regression was used to assess the relationship between exposure to PM 2.5 and its constituents and the risk of GDM, and the expected inflection point between exposure to PM 2.5 and its constituents and the risk of GDM was estimated using logistic regression combined with restricted cubic spline curves. Stratified analyses and interaction tests were performed.

After adjustment for confounders, exposure to PM 2.5 and its constituents (NO 3 − , NH 4 + , and OM) was positively associated with the risk of GDM during pregnancy, especially when exposure to NO 3 − and NH 4 + occurred in the first to second trimester, with each interquartile range increase the risk of GDM by 20.2% (95% CI: 1.118–1.293) and 18.2% (95% CI. 1.107–1.263), respectively. The lowest inflection points between PM 2.5 , SO 4 2− , NO 3 − , NH 4 + , OM, and BC concentrations and GDM risk throughout the gestation period were 18.96, 5.80, 3.22, 2.67, 4.77 and 0.97 µg/m 3 , respectively. In the first trimester, an age interaction effect between exposure to SO 4 2− , OM, and BC and the risk of GDM was observed.

Conclusions

This study demonstrates a positive association between exposure to PM 2.5 and its constituents and the risk of GDM. Specifically, exposure to NO 3 − , NH 4 + , and OM was particularly associated with an increased risk of GDM. The present study contributes to a better understanding of the effects of exposure to PM 2.5 and its constituents on the risk of GDM.

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Gestational diabetes mellitus (GDM) is a common metabolic disorder of pregnancy, and its incidence has increased in recent years. It is estimated that GDM affects approximately 16.7% of pregnancies worldwide, affecting approximately 21 million live births, and in China, the prevalence of GDM has reached 8.6% [ 1 ]. GDM affects not only the health of pregnant women, [ 2 , 3 , 4 ] but also the potential occurrence of adverse pregnancy outcomes, including macrosomia and neonatal hypoglycaemia, and increases the long-term risk of diabetes in both mothers and children [ 5 , 6 , 7 ]. Therefore, to reduce the risk of GDM and its associated complications, it is particularly important to study the pathogenic factors of GDM.

The mechanisms through which fine particulate matter (PM 2.5 ) exposure leads to GDM are not fully understood and may involve multiple pathways that increase the risk of GDM. Animal experiments by Xu J et al. have shown that PM 2.5 exposure in mice induces oxidative stress mediated by nuclear factor erythroid 2 related factor 2 and activates inhibitory signaling pathways mediated by c-Jun N-terminal kinase, leading to hepatic insulin resistance (IR). [ 8 ] PM 2.5 contains thousands of chemical constituents, with polycyclic aromatic hydrocarbons (PAHs) being the most prominent organic constituents. Research suggests that lipophilic PAHs may contribute to IR through methylation-mediated suppression of the insulin receptor substrate 2 gene. [ 9 ] Additionally, PM 2.5 also interferes with the inflammatory response in visceral adipose tissue, lipid metabolism in hepatocytes and glucose metabolism in skeletal muscle by altering the CC-chemokine receptor 2 signalling pathway, further exacerbating insulin resistance. [ 10 ] An increasing number of studies suggest that exposure to PM 2.5 is associated with an increased risk of developing diabetes [ 11 , 12 , 13 , 14 ]. According to a study of 395,927 pregnant women in southern California, exposure to ambient PM 2.5 increases the likelihood of developing gestational diabetes mellitus (GDM) [ 15 ]. A case‒control study by Shen HN et al. [ 16 ] revealed that exposure to PM 2.5 in early and mid-pregnancy increased the risk of GDM by 9% (95% CI 1.02‒1.17) and 7% (95% CI 1.01‒1.14), respectively. A positive association between PM 2.5 exposure in the second trimester and GDM risk was found in a study of 2,078,669 people in Florida between 2005 and 2015 [ 17 ]. However, there is also evidence that exposure to PM 2.5 is not associated with an increased risk of GDM. [ 18 , 19 ] Therefore, the relationship between PM 2.5 exposure and the risk of gestational diabetes is controversial and needs to be clarified by further large-scale studies.

PM 2.5 is composed of a variety of substances, including sulfate (SO 4 2− ), nitrate (NO 3 − ), ammonium (NH 4 + ), organic matter (OM), and black carbon (BC). The toxicity of PM 2.5 constituents to people is variable. Wang X et al. [ 20 ] conducted a study on PM 2.5 constituents and asthma in six low- and middle-income countries and found that ammonia may be the main cause of asthma. Li S et al. [ 21 ] conducted a large-scale epidemiological survey in Southwest China and showed that OM may be the main cause of the association between PM 2.5 exposure and diabetes mellitus risk. BC and OM were found to be the PM 2.5 constituents that are most strongly and consistently associated with cardiovascular mortality and morbidity. [ 22 ] However, evidence on the relationship between exposure to PM 2.5 constituents and GDM risk is limited. Previous studies have focused on the relationship between PM 2.5 exposure and GDM risk, and a further understanding of the relationship between exposure to different PM 2.5 constituents and the risk of GDM could rationally explain which component is responsible for the relationship between PM 2.5 exposure and GDM risk and provide new opportunities to reduce the burden of GDM associated with PM 2.5 exposure.

To address the research needs in this area, this retrospective cohort study evaluated the association of exposure to PM 2.5 and its constituents with the risk of GDM in a population from Guangzhou city, Guangdong Province, China, to provide a basis for the targeted prevention and control of PM 2.5 constituents.

Study cohort

This retrospective study focused on pregnant women who visited the Maternal and Children Health Care Hospital of Huadu in Guangzhou between 2020 and 2022. This specialized hospital primarily serves pregnant women and children, and its services cover the entire Guangzhou territory. The data of the study participants were obtained from the electronic case management system of the hospital, and GDM diagnoses were made according to the ICD-10 classification criteria for participants with diagnosis code O24. Participants who met the following criteria were included in the study: lived in Guangzhou during pregnancy, had complete relevant data, were not pregnant with twins, had no history of diabetes or hypertension before pregnancy, and conceived naturally. Notably, as this study used deidentified information, it was not necessary to obtain informed consent. This study was approved by the Ethics Committee of t the Maternal and Child Health Hospital of Huadu District (No. 2024-001).

Exposure to PM 2.5 and its constituents

To obtain daily concentrations of PM 2.5 and its constituents, including SO 4 2− , NO 3 − , NH 4 + , OM, and BC, at a spatial resolution of 10 km × 10 km, we used data from the Tracking Air Pollution in China (TAP) project. This dataset, accessible via the web portal ( http://tapdata.org.cn ), consolidates ground-level measurements from various publications and supplements them with satellite-derived estimates. The estimation process used aerosol optical depth (AOD) data in conjunction with the GEOS-Chem atmospheric chemistry transport model, as described by Liu et al. [ 23 ] The temperature and relative humidity data used in this study were obtained from a website ( https://rp5.ru/ ), and the monitoring site used was Guangzhou Airport.

To assess the exposure concentrations of PM 2.5 and its constituents for each study participant, we used the time-varying average concentration method. Specifically, since all participants lived in Guangzhou, we first collected daily average concentrations of PM 2.5 and its constituents for the city. Using the average for the entire region and each participant’s gestational week and delivery date, we estimated their average exposure concentrations during the first trimester (1–13 gestational weeks, T1), the second trimester (14–28 gestational weeks, T2), and first to second trimester (T1 + T2).

Based on earlier studies [ 24 , 25 ] and information obtained from electronic medical records, we selected potential confounders, including age, ethnicity, occupation type, marital status, blood type, nonprimiparous status, anaemia status, infant weight, preeclampsia status, vaginitis status, gestational hypertension status, thyroid disease status, temperature, and relative humidity. Participants self-reported their ethnicity (Han, Hui, Miao, Tujia, etc.), occupation type (employee, civil servant, professional, self-employed, farmer, unemployed, etc.), marital status (married, divorced), blood type (A, B, O, AB), and whether they were first-time mothers or had given birth to at least one child. Ethnicity was reclassified as Han or other; occupation type was reclassified as employed, self-employed, or other; and infant weight was classified as low birth weight (< 2500 g), normal birth weight (2500–4000 g), or macrosomia (> 4000 g) based on the recorded birth weight. Assessing exposure to temperature and relative humidity using the same methodology as for PM 2.5 and its constituents.

Diagnosis of GDM

According to the diagnostic criteria for GDM, [ 26 , 27 ] all pregnant women underwent oral glucose tolerance tests after fasting for at least 8 h between the 24th and 28th weeks of pregnancy. During the test, the pregnant woman had to drink 300 ml of a solution containing 75 g of glucose within 5 min. Blood glucose levels were measured before, 1 h after, and 2 h after glucose ingestion. According to medical guidelines, the blood glucose levels of pregnant women should be kept below 5.1 mmol/L, 10.0 mmol/L and 8.5 mmol/L at these three times. If a pregnant woman’s blood glucose level meets or exceeds any of the above criteria, she will be diagnosed with GDM by a health care professional.

Statistical analyses

We used chi-squared or nonparametric tests for baseline characteristics. and Spearman’s rank correlation test was used to assess the correlations between exposure to PM 2.5 and its constituents. Logistic regression analyses were used to estimate the odds ratios (ORs) and 95% confidence intervals (95% CIs) associated with the development of GDM, adjusting for potential confounders, including age, ethnicity, occupation type, marital status, blood type, nonprimary status, anaemia status, infant weight, preeclampsia status, vaginitis status, gestational hypertension status, thyroid disease status, temperature, and relative humidity. We used a logistic regression combined with restricted cubic spline curves to assess the relationship between exposure to PM 2.5 and its constituents and the risk of GDM, with the reference value (OR = 1) set at the 10th percentile and the nodes set at the 5th, 35th, 65th, and 95th percentiles of the concentrations of PM 2.5 and its constituents. Furthermore, we conducted stratified analyses to evaluate the impact of exposure to PM 2.5 and its constituents on GDM risk.

Statistical analyses were performed with STATA 16.0 (StataCorp, USA) and R 4.3.2 (Lucent Technologies, USA) using the “rcssci” and “autoReg” packages. A two-tailed p  < 0.05 was considered to indicate statistical significance.

Baseline characteristics

In total, 17,855 pregnant women were included in our study, and 22.14% of the participants had GDM. The median (P25, P75) age of the participants was 29 years (26 years, 33 years), and 14.86% of the pregnant women were of an advanced maternal age. The median exposure concentrations for PM 2.5 , SO 4 2− , and OM in the GDM group were greater than those in the non-GDM group, and the temperature and relative humidity in the GDM group were greater than those in the non-GDM group. Further details are shown in Table  1 .

Correlation analysis of PM 2.5 , SO 4 2− , NO 3 − , NH 4 + , OM, and BC concentrations

Table  2 shows the concentrations of PM 2.5 , SO 4 2− , NO 3 − , NH 4 + , OM, and BC during the study period. There was a strong correlation among PM 2.5 , SO 4 2− , NO 3 − , NH 4 + , OM, and BC concentrations (Spearman’s correlation coefficient > 0.8). To ensure that the results of the correlation analysis were not affected by outliers, we performed a sensitivity analysis. Specifically, we chose the 95th percentile of PM 2.5 concentration as a threshold to exclude extreme values from the dataset and recalculated the correlation coefficients. We found that the correlation coefficients between PM 2.5 and its components did not significantly change after removing the extreme values (Table S1 ).

Relationship between PM 2.5 , SO 4 2− , NO 3 − , NH 4 + , OM, and BC exposure and GDM risk

Table  3 shows the associations between exposure to PM 2.5 , SO 4 2− , NO 3 − , NH 4 + , OM, and BC and the risk of GDM. After adjusting for confounding factors, in the first trimester, the ORs per Interquartile range (IQR) increase in PM 2.5 , SO 4 2− , NO 3 − , NH 4 + , OM, and BC concentrations were associated with an increase in the risk of GDM by 9.2% (95% CI: 1.034–1.154), 8. 6% (95% CI: 1.035–1.140), 11.6% (95% CI: 1.034–1.023), 11.1% (95% CI: 1.037–1.190), 9.7% (95% CI: 1.040–1.158), and 8.5% (95% CI: 1.039–1.134), respectively. Exposure to PM 2.5 , NO 3 − , NH 4 + , and OM in the second trimester and exposure to PM 2.5 , SO 4 2− , NO 3 − , NH 4 + , OM, and BC from the first to second trimester also increased the risk of GDM.

After adjusting for confounders, we found that the inflection points between PM 2.5 , OM, and BC concentrations and GDM risk were lowest in the second trimester, at 18.96, 4.77 and 0.97 µg/m 3 , respectively. The inflection points between SO 4 2− , NO 3 − and NH 4 + concentrations and GDM risk were lowest in the first to second trimester, at 5.80, 3.22 and 2.67 µg/m 3 , respectively. In addition, a nonlinear relationship between PM 2.5 , NO 3 − , NH 4 + , and OM exposure and GDM risk was observed only in the first trimester (p values for nonlinearity of 0.002, 0.008, 0.001 and 0.022, respectively) (Figs.  1 , 2 Figure S1 - S2 ).

Flowchart of participant screening

Association between predicted exposure to PM 2.5 and its constituents during the first trimester and GDM risk. The solid line indicates the OR, and the dashed area indicates the 95% CI. The reference point is the lowest value for PM 2.5 and its constituents, and the nodes are at the 5th, 35th, 65th, and 95th percentiles for PM 2.5 and its constituents

Subgroup analysis

To evaluate the association between exposure to PM 2.5 and its constituents and GDM risk, stratified and interaction analyses of the study participants’ age, ethnicity, occupation type, marital status, blood type, nonprimiparous status, anaemia status and infant sex were performed. In the first trimester, significant associations between PM 2.5 , SO 4 2− , NO 3 − , NH 4 + , OM and BC exposure and GDM risk were observed in the nonprimiparous, anaemic and infant sex subgroups ( p  < 0.05) (Table  4 ; Fig.  3 , Table S2 - S5 ). A similar pattern of increased GDM risk was found in the second trimester and the first to second trimester subgroups. Details of the exposure effect sizes for the second trimester subgroup are given in Tables S6-S11. The exposure effect sizes for the first to second trimester subgroup are presented in Tables S12 - S17 . In addition, an interaction by age subgroup was observed only between exposure to SO 4 2− , OM and BC in the first trimester and GDM risk (p values for the interaction were 0.046, 0.046 and 0.044, respectively).

Forest plot of subgroup analysis of the relationship between SO 4 2− exposure in the first trimester and GDM risk

In this study, we found that exposure to the air pollutant PM 2.5 and its constituents (SO 4 2− , NO 3 − , NH 4 + , OM and BC) is positively associated with an increased risk of GDM. In addition, nonlinear associations were found between PM 2.5 , NO 3 − , NH 4 + , OM exposure during the first trimester and GDM risk, while subgroup analyses revealed age interactions between exposure to SO 4 2− , OM and BC during the first trimester and GDM risk.

Numerous epidemiological studies have consistently revealed a correlation between exposure to PM 2.5 and the risk of GDM, [ 28 , 29 , 30 ] which is consistent with the findings of this study. Tang et al. [ 31 ] analysed 13 studies (including 9 retrospective studies, 3 prospective studies and 1 case‒control study) and found that PM 2.5 exposure in the second trimester was associated with an increased risk of GDM (OR 1.07, 95% CI 1.00 to 1.13), while PM 2.5 exposure in the first trimester did not increase the risk of GDM (OR 1.01; 95% CI 0.96 to 1.07). A retrospective cohort study conducted in Shanghai, China, from 2014 to 2016 revealed that a 10 µg/m 3 increase in PM 2.5 exposure during the first trimester, second trimester, and first to second trimester increased the risk of GDM by 9% (95% CI: 1.02, 1.16), 9% (95% CI: 1.03, 1.16), and 15% (95% CI: 1.04, 1.28), respectively. [ 32 ] However, a study from Hebei, China, showed that PM 2.5 exposure in the first trimester, second trimester, or first to second trimester did not increase the risk of GDM. [ 33 ] The results of this study showed that exposure to PM 2.5 increased the risk of GDM by 9.2% (95% CI: 1.034–1.154), 8.2% (95% CI: 1.014–1.154), and 10.5% (95% CI: 1.046–1.167) in the first, second, and first to second trimester, respectively. This finding is consistent with a previous study conducted in Foshan city, Guangdong Province, from 2015 to 2019, which was a birth cohort study. The results showed that exposure to PM 2.5 during the first, second, and first to second trimester increased the risk of GDM [ 34 ]. This may be due to the proximity of Foshan to Guangzhou and their similar geographical and climatic conditions. Such similarities could result in comparable sources, concentrations and compositions of PM 2.5 pollution in both areas, leading to consistent research results between the two locations. In addition, similarities in residents’ lifestyles, dietary habits and other factors may contribute to similar sensitivities to PM 2.5 exposure and susceptibility to GDM, further explaining the consistency of the research findings.

Strong seasonal and regional variations in PM 2.5 constituents were suggested by Bell et al. [ 35 ] However, it is still unclear which PM 2.5 constituents have the greatest effect on GDM risk, and research on the association between exposure to PM 2.5 constituents and the risk of GDM remains limited. A cross-sectional survey conducted in 55 hospitals across 24 provinces in China from 2015 to 2016, with a total of 54,517 participants, revealed that organic compounds, black carbon, and nitrate may be the main causes behind the occurrence of GDM. [ 36 ] A retrospective cohort study conducted in the United States between 2002 and 2008 involving 201,015 participants revealed that each IQR increase in nitrate exposure during the first trimester was associated with a 5% (95% CI: 1.02–1.09) increased risk of GDM. However, exposure to elemental carbon, organic compounds, ammonium ions and sulfate did not increase the risk of GDM. [ 37 ] A recent meta-analysis of 31 eligible cohort studies revealed that second-trimester BC exposure and first-trimester NO 3 − exposure increased the risk of GDM, with RRs of 1.128 (1.032–1.231) and 1.128 (1.032–1.231), respectively. A recent meta-analysis of 31 eligible cohort studies revealed that NO 3 − exposure in the first trimester and BC exposure in the first to second trimester increased the risk of GDM by 5.6% (95% CI: 1.008–1.107) and 18.5% (95% CI: 1.026–1.368), respectively [ 38 ]. This finding is not entirely consistent with our findings in this retrospective cohort study, which revealed that although SO 4 2− and BC exposure in the second trimester was negatively associated with GDM risk, SO 4 2− , NO 3 − , NH 4 + , OM, and BC exposure in other exposure windows were positively associated with GDM risk. The reason for this inconsistency may be due to significant variations in the levels of exposure to PM 2.5 and its constituents in different countries and regions, as well as significant differences in the methods used to assess the exposure levels of the study participants.

Previous studies on the relationship between exposure to PM 2.5 and its constituents and the risk of GDM have focused on risk assessment and exposure windows, [25, 39, 40]while the critical concentrations defining the association between these variables have been less explored. This study provides clearer evidence for the prevention of GDM in individuals with exposure to PM 2.5 and its constituents by analysing the cut-off values of PM 2.5 and its constituents associated with the occurrence of GDM. This study also provides a more precise basis for targeted interventions and policy development. In addition, we investigated the potential impacts of age, ethnicity, occupation type, marital status, blood type, nonprimiparous status, anaemia status, and infant sex. Our findings revealed a statistically significant age interaction between exposure to SO 4 2− , OM, and BC during the first trimester and the risk of GDM. Our results revealed a statistically significant age interaction effect between SO 4 2− , OM and BC exposure in the first trimester and GDM risk. This may be due to several factors. First, pregnant women of different ages have marked physiological differences, such as variations in metabolic rate, hormone levels and organ function, which may lead to different sensitivities to PM 2.5 constituents. Second, with increasing age, prolonged exposure to environmental pollutants and the adoption of unhealthy lifestyles may increase the susceptibility of pregnant women to air pollutants in early pregnancy, thereby increasing the risk of GDM. Third, differences in prenatal nutrition, health care, work and family stress among age groups may differentially affect pregnant women’s susceptibility to air pollution. Finally, age-related changes in the immune system may lead to different immune responses to air pollutants in pregnant women. Such differences could increase the susceptibility of certain age groups to the effects of air pollutants, thereby increasing the likelihood of GDM.

There are a number of advantages to this study. First, the study population consists of pregnant women from Guangzhou, a large city in China, with a large sample size covering all 11 administrative districts of the city, which enhances the generalisability and applicability of the results. Second, we used logistic regression combined with restricted cubic splines, a method that allows us to accurately capture the exposure-response relationship and its non-linear effects. Finally, we adjusted the analysis for various confounding factors, such as age, ethnicity, occupation, marital status and blood group, and conducted subgroup analyses to explore heterogeneity among different subgroups. These measures increase the credibility of the results and provide new directions for future research. However, several limitations of this study need to be considered. First, there is a potential risk of exposure misclassification, as individual mobility was not taken into account during the exposure assessment, which may have affected the accuracy of the exposure estimates. Second, the cut-off for defining the onset of GDM in our study population was set at 28 weeks gestation rather than the clinically meaningful threshold of 24 weeks. This extended timeframe may have introduced ambiguity, potentially weakening the directness and clarity of the association between exposures and outcomes. Additionally, this study used a spatial resolution of 10 km x 10 km to estimate exposure to PM2.5 and its components, and the low spatial resolution of the exposure assessment may not be fine enough in some areas, especially localised urban pollution hotspots, which may affect the precision of the exposure estimates for the study population.

Our results suggest that exposure to SO 4 2− and BC during mid-pregnancy is negatively associated with GDM risk, whereas exposure to PM 2.5 and its constituents during other windows is positively associated with an increased risk of GDM, adding to the evidence on the effects of exposure to PM 2.5 and its constituents on the development of GDM. Furthermore, we identified thresholds for the effects of exposure to PM 2.5 and its constituents on the risk of GDM during different exposure periods. These results have important implications for the prevention of GDM and call for further research to confirm our findings and elucidate the underlying mechanisms involved.

Data availability

No datasets were generated or analysed during the current study.

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Weiqi Liu, Haidong Zou and Weiling Liu contributed equally to this work.

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Department of Clinical Laboratory, The Maternal and Children Health Care Hospital (Huzhong Hospital) of Huadu, Guangzhou, 510800, Guangdong, People’s Republic of China

Department of Obstetrics, The Maternal and Children Health Care Hospital (Huzhong Hospital) of Huadu, Guangzhou, 510800, Guangdong, People’s Republic of China

Haidong Zou & Jiangxia Qin

Department of Clinical Laboratory, Foshan Fosun Chancheng Hospital, Foshan, 528000, Guangdong, People’s Republic of China

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Weiqi Liu conceived the study. Weiqi Liu and Weiling Liu drafted the manuscript. Weiqi Liu performed formal analyses, investigation, methodology, software and verification. Weiqi Liu and Haidong Zou revised the manuscript. Jiangxia Qin and Haidong Zou supported data collection. All authors participated in the interpretation of the results and approved the final version of the manuscript.

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Liu, W., Zou, H., Liu, W. et al. The impact of PM 2.5 and its constituents on gestational diabetes mellitus: a retrospective cohort study. BMC Public Health 24 , 2249 (2024). https://doi.org/10.1186/s12889-024-19767-1

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M.P. is a 30-year-old man who was diagnosed with type 2 diabetes 2 weeks before admission to the hospital. He has a strong family history of type 2 diabetes. He smokes heavily (> 20 cigarettes/day) and admits to some alcohol consumption. His primary care physician had started him on metformin,500 mg three times daily. There were no complications of diabetes at the time of presentation. Two days before his admission, he developed generalized abdominal discomfort, watery diarrhea, and bilious vomiting. He denied any history suggestive of food poisoning or recent surgery. He was apyrexial on admission. His blood pressure was 170/101 mmHg, pulse was 100 bpm, and temperature was 98.9°F. There were no signs suggestive of peritonitis, and his abdomen was soft without guarding. He had deep-seated tenderness in the epigastric region. Initial investigations showed a white blood count of 25.9× 109, hemoglobin of 15.8 g/dl, and C-reactive protein (CRP) of 200 mg. Renal functions were normal, and liver function tests were normal except for an increased lactic acid dehydrogenase (LDH) level of 848 IU/l.

Chest X-ray showing air under diaphragm (arrows).

His metformin was stopped because his gastrointestinal symptoms were attributed to metformin. After stopping metformin, he was able to eat normally and tolerate a regular diet. He received subcutaneous insulin therapy to control his glucose levels. He continued to have some abdominal discomfort,however, and on questioning reported right shoulder pain. Shoulder examination showed no signs of inflammation. There was no restriction of movement at the right shoulder. A chest X-ray appeared to demonstrate free air beneath the diaphragm.

Why is this patient having abdominal discomfort?

What investigation would confirm the diagnosis?

How should this condition be managed?

M.P. has pneumoperitoneum (PP). PP is the presence of air within the peritoneal cavity. Most commonly, it is caused by perforated viscous(perforated gastric ulcer, bowel perforation, tumour, or trauma). PP from perforation is associated with peritonitis in most cases. 1   In this setting, sign and symptoms of peritonism are present, and patients require prompt surgical intervention. On rare occasion, PP may occur without gastrointestinal perforation. Trauma, recent surgery, barotraumas, mechanical ventilation, diagnostic procedures (e.g., endoscopy and colposcopy) are other causes of spontaneous PP without peritonitis. The cause is often identifiable from the patient's history, but in this case it was not.

Nonsurgical PP is PP that is not associated with signs of peritonitis. Patients with nonsurgical PP present with vague abdominal symptoms but do not have signs suggestive of acute abdomen. Nonsurgical PP can occur in a number of situations (e.g., silent self-sealing perforation as in patients with diabetes, patients receiving steroids, elderly patients, or critically ill patients). Deliberate air introduction into the peritoneum could also account for nonsurgical PP, as in the case of peritoneal dialysis, injury to the female genital tract (e.g., from skiing accidents), postendoscopy leakage, or ruptured pneumatosis intestinal cysts. 2  

The pain in the right shoulder noted in this case was likely referred pain. Initial abdominal discomfort and diarrhea was attributed to metformin-induced gastrointestinal side effects. When the symptoms persisted even after metformin was discontinued, search for another cause of abdominal pain revealed the correct diagnosis. This case illustrates the need to continue searching for other causes of gastrointestinal distress when symptoms do not resolve after stopping metformin. M.P.'s ultrasound scan of the abdomen showed the presence of fatty liver consistent with heavy alcohol abuse. It is also noteworthy that metformin is contraindicated in alcohol abuse and in patients at risk for dehydration. This patient, therefore, had multiple reasons to discontinue metformin. The chest X-ray showed the presence of air under the diaphragm ( Figure 1 ). An abdominal computed tomography (CT) scan showed the presence of free air within the peritoneal cavity ( Figure 2 ). There was no evidence of viscous perforation on further barium studies.

Abdominal CT scan showing presence of air within the peritoneal cavity. Arrow points to the falciparum ligament made prominent by the presence of air on both sides.

PP unaccompanied by peritonitis is usually asymptomatic. It can be diagnosed by erect chest X-ray showing the presence of air under the diaphragm. Abdominal CT scanning is the gold standard for confirming the diagnosis of PP. The CT scan is a sensitive tool and demarcates air within the peritoneal space. Once the diagnosis is confirmed, further investigations should be directed to uncover the cause and source of the air leak. Barium studies may show the perforation unless it is too small or has sealed spontaneously.

M.P. was managed conservatively. He was monitored closely. He tolerated normal meals. His diarrhea stopped. His CRP remained elevated for 2 weeks and then normalized at the time of discharge. Other markers of inflammation also improved in the same time period. Abdominal discomfort improved in 2 weeks. His blood glucose stabilized with insulin therapy. He remained fully mobile and independent and was discharged to home. He has not had any recurrences.

Nonsurgical PP has been described in the literature. Isolated cases from different pathophysiological origins have been reported. 3 - 5   Nonsurgical PP masquerading as metformin-induced gastrointestinal upset has not been reported previously. In addition to recognizing and diagnosing PP, it is important to be aware of rare nonsurgical causes of PP. Unnecessary surgery can be avoided in such cases.

Clinical Pearls

PP does not always require surgical intervention. In hemodynamically stable, minimally symptomatic patients, unusual causes of PP should be considered to avoid unnecessary surgery.

Patients with diabetes may have selflimiting small perforations that seal spontaneously. Absence of clinical signs of peritonism and the medical history of the patient can point to the nonsurgical nature of the condition.

Metformin can cause gastrointestinal upset, but other causes of such symptoms should be diligently searched for if patients remain symptomatic after stopping metformin.

Ranjna Garg, MRCP, MD, is specialist registrar at University Hospital in Birmingham, U.K.

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What Every Provider Needs to Know About Type 1 Diabetes

Miriam E. Tucker

August 16, 2024

In July 2024, a 33-year-old woman with type 1 diabetes was boating on a hot day when her insulin delivery device slipped off. By the time she was able to exit the river, she was clearly ill, and an ambulance was called. The hospital was at capacity. Lying in the hallway, she was treated with fluids but not insulin, despite her boyfriend repeatedly telling the staff she had diabetes. She was released while still vomiting. The next morning, her boyfriend found her dead.

This story was shared by a friend of the woman in a Facebook group for people with type 1 diabetes and later confirmed by the boyfriend in a separate heartbreaking post. While it may be an extreme case, encounters with a lack of knowledge about type 1 diabetes in healthcare settings are quite common, sometimes resulting in serious adverse consequences.

In my 50+ years of living with the condition, I've lost track of the number of times I've had to speak up for myself, correct errors, raise issues that haven't been considered, and educate nonspecialist healthcare professionals about even some of the basics.

Type 1 diabetes is an autoimmune condition in which the insulin-producing cells in the pancreas are destroyed, necessitating lifelong insulin treatment. Type 2, in contrast, arises from a combination of insulin resistance and decreased insulin production. Type 1 accounts for just 5% of all people with diabetes, but at a prevalence of about 1 in 200, it's not rare. And that's not even counting the adults who have been misdiagnosed as having type 2 but who actually have type 1.

As a general rule, people with type 1 diabetes are more insulin sensitive than those with type 2 and more prone to both hyper- and hypoglycemia. Blood sugar levels tend to be more labile and less predictable, even under normal circumstances. Recent advances in hybrid closed-loop technology have been extremely helpful in reducing the swings, but the systems aren't foolproof yet. They still require user input (ie, guesswork), so there's still room for error.

Managing type 1 diabetes is challenging even for endocrinologists. But here are some very important basics that every healthcare provider should know:

We Need Insulin 24/7

Never, ever withhold insulin from a person with type 1 diabetes, for any reason. Even when not eating — or when vomiting — we still need basal (background) insulin, either via long-acting analog or a pump infusion. The dose may need to be lowered to avoid hypoglycemia, but if insulin is stopped, diabetic ketoacidosis will result. And if that continues, death will follow.

This should be basic knowledge, but I've read and heard far too many stories of insulin being withheld from people with type 1 in various settings, including emergency departments, psychiatric facilities, and jails. On Facebook, people with type 1 diabetes often report being told not to take their insulin the morning before a procedure, while more than one has described "sneaking" their own insulin while hospitalized because they weren't receiving any or not receiving enough.

On the flip side, although insulin needs are very individual, the amount needed for someone with type 1 is typically considerably less than for a person with type 2. Too much can result in severe hypoglycemia. There are lots of stories from people with type 1 diabetes who had to battle with hospital staff who tried to give them much higher doses than they knew they needed.

The American Diabetes Association recommends that people with type 1 diabetes who are hospitalized be allowed to wear their devices and self-manage to the degree possible. And please, listen to us when we tell you what we know about our own condition.

Fasting Is Fraught

I cringe every time I'm told to fast for a test or procedure. Fasting poses a risk for hypoglycemia in people with type 1 diabetes, even when using state-of-the-art technology. Fasting should not be required unless absolutely necessary, especially for routine lab tests.

Saleh Aldasouqi, MD, chief of endocrinology at Michigan State University, East Lansing, Michigan, has published several papers on a phenomenon he calls "Fasting-Evoked En Route Hypoglycemia in Diabetes," in which patients who fast overnight and skip breakfast experience hypoglycemia on the way to the lab.

"Patients continue taking their diabetes medication but don't eat anything, resulting in low blood sugar levels that cause them to have a hypoglycemic event while driving to or from the lab, putting themselves and others at risk," Aldasouqi explained, adding that fasting often isn't necessary for routine lipid panels .

If fasting is necessary, as for a surgical procedure that involves anesthesia, the need for insulin adjustment — NOT withholding — should be discussed with the patient to determine whether they can do it themselves or whether their diabetes provider should be consulted.

But again, this is tricky even for endocrinologists. True story: When I had my second carpal tunnel surgery in July 2019, my hand surgeon wisely scheduled me for his first procedure in the morning to minimize the length of time I'd have to fast. (He has type 1 diabetes himself, which helped.) My endocrinologist had advised me, per guidelines, to cut back my basal insulin infusion on my pump by 20% before going to bed.

But at bedtime, my continuous glucose monitor (CGM) showed that I was in the 170 mg/dL's and rising, not entirely surprising since I'd cut back on my predinner insulin dose knowing I wouldn't be able to eat if I dropped low later. I didn't cut back the basal.

When I woke up, my glucose level was over 300 mg/dL. This time, stress was the likely cause. (That's happened before.) Despite giving myself several small insulin boluses that morning without eating, my blood sugar was still about 345 mg/dL when I arrived at the hospital. The nurse told me that if it had been over 375 mg/dL, they would have had to cancel the surgery, but it wasn't, so they went ahead. I have no idea how they came up with that cutoff.

Anyway, thankfully, everything went fine; I brought my blood sugar back in target range afterward and healed normally. Point being, type 1 diabetes management is a crazy balancing act, and guidelines only go so far.

We Don't React Well to Steroids

If it's absolutely necessary to give steroids to a person with type 1 diabetes for any reason, plans must be made in advance for the inevitable glucose spike. If the person doesn't know how to adjust their insulin for it, please have them consult their diabetes provider. In my experience with locally injected corticosteroids, the spike is always higher and longer than I expected. Thankfully, I haven't had to deal with systemic steroids, but my guess is they're probably worse.

Procedures Can Be Pesky

People who wear insulin pump and/or CGMs must remove them for MRI and certain other imaging procedures. In some cases — as with CGMs and the Omnipod insulin delivery device that can't be put back on after removal — this necessitates advance planning to bring along replacement equipment for immediately after the procedure.

Diabetes devices can stay in place for other imaging studies, such as X-rays, most CT scans, ECGs, and ultrasounds. For heaven's sake, don't ask us to remove our devices if it isn't totally necessary.

In general, surprises that affect blood sugar are a bad idea. I recently underwent a gastric emptying study. I knew the test would involve eating radioactive eggs, but I didn't find out there's also a jelly sandwich with two slices of white bread until the technician handed it to me and told me to eat it. I had to quickly give myself insulin, and of course my blood sugar spiked later. Had I been forewarned, I could have at least "pre-bolused" 15-20 minutes in advance to give the insulin more time to start working.

Another anecdote: Prior to a dental appointment that involved numbing my gums for an in-depth cleaning, my long-time dental hygienist told me "be sure to eat before you come." I do appreciate her thinking of my diabetes. However, while that advice would have made sense long ago when treatment involved two daily insulin injections without dose adjustments, now it's more complicated.

Today, when we eat foods containing carbohydrates, we typically take short-acting insulin, which can lead to hypoglycemia if the dose given exceeds the amount needed for the carbs, regardless of how much is eaten. Better to not eat at all (assuming the basal insulin dose is correct) or just eat protein. And for the provider, best to just tell the patient about the eating limitations and make sure they know how to handle.

Duh, We Already Have Diabetes

I've heard of at least four instances in which pregnant women with type 1 diabetes have been ordered to undergo an oral glucose tolerance test to screen for gestational diabetes. In two cases, it was a "can you believe it?!" post on Facebook, with the women rightly refusing to take the test.

But in May 2024, a pregnant woman reported she actually drank the liquid, her blood sugar skyrocketed, she was vomiting, and she was in the midst of trying to bring her glucose level down with insulin on her own at home. She hadn't objected to taking the test because "my ob. gyn. knows I have diabetes," so she figured it was appropriate.

I don't work in a healthcare setting, but here's my guess: The ob. gyn. Hadn't actually ordered the test but had neglected to UN-order a routine order for a pregnant patient who already had diabetes and obviously should NOT be forced to drink a high-sugar liquid for no reason. If this is happening in pregnancies with type 1 diabetes, it most certainly could be as well for those with preexisting type 2 diabetes. Clearly, something should be done to prevent this unnecessary and potentially harmful scenario.

In summary, I think I speak for everyone living with type 1 diabetes in saying that we would like to have confidence that healthcare providers in all settings can provide care for whatever brought us to them without adding to the daily burden we already carry. Let's work together.

Reviewed by Saleh Aldasouqi, MD, chief of endocrinology at Michigan State University.

Miriam E. Tucker is a freelance journalist based in the Washington, DC, area. She is a regular contributor to Medscape Medical News, with other work appearing in the Washington Post, NPR's Shots blog, and Diatribe. She is on X @MiriamETucker.

Send comments and news tips to [email protected] .

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