Furthermore, the study excluded women with self-reported severe nausea and vomiting as well as those who were pregnant or lactating prior to recruitment. Moreover, we excluded women who were put on specific diet therapies or had been restricted from consuming a variety of foods.

The study sample size was computed based on a hypothesized correlation coefficient of 0. Based on an established protocol for estimating sample sizes in cross-sectional studies by Rosner [ 36 ], a total of women were first computed. In order to increase the power of the statistical results and to cater for non-response rate the was multiplied by 1.

kg; 22, Hamburg, Germany; Model: ; designed in Germany-made in China. Height measurement was by the use of United Nations Children Emergency Fund height board.

The values were recorded to the nearest 0. We calculated BMI as weight in kilograms divided by the height in meters squared.

BMI values of 25— The WC was measured based on World Health Organization guidelines thus, at the mid-point between the lower border of the rib cage and the iliac crest using a non-stretchable fiber-glass measuring tape.

All WC values were recorded to the nearest 0. We calculated WHtR by dividing WC cm by the measured height cm. Information about demographic and lifestyle factors such as age, educational status, marital status, occupation, household size, household assets, parity, and physical activity were obtained using a structured questionnaire following face-to-face interviews.

In order to estimate their economic status, we enumerated household assets for households that the selected study participants belong. Based on the list of items reported and the make-up of their rooms, we calculated a summary value as a proxy for economic status. Physical activity level of participants was measured using the International Physical Activity Questionnaire short form [ 40 ].

The reliability and validity of these questionnaires were assessed across 12 countries and the results showed they can be applied in many settings and in different languages [ 41 ].

All dietary data were obtained based on repeated non-consecutive 2-day h dietary recalls. We used real food items, food models and standard kitchen weighing equipment to guide food portion size estimation. We deployed a trained caterer for the interviews. Absolute quantities of dietary fatty acids were determined using standard portion sizes of food items from the United States Department of Agriculture Food Composition Databases [ 42 ] due to a lack of local database on this issue.

For the same reason, we obtained melting points of dietary fatty acids from the Japanese Lipid Bank database [ 43 ]. The mean values of daily intake of total fat, fatty acids, and energy for the two days were used in the analysis. Also, we calculated DFQ indices from established protocols as detailed below.

The lipophilic index of dietary intake was computed by multiplying the intake of each fatty acid in grams by its specific melting point °C , adding the products, and then dividing by the total of fatty acid intake in grams [ 34 ].

All data were checked for compliance with the selected statistical techniques. The Kolmogorov—Smirnov test was used to evaluate the normality of the data distribution. To use physical activity as a continuous variable in our regression models, it was first transformed natural logarithm because it was positively skewed.

Out of the subjects sampled for the study, we excluded 18 participants because their reported dietary energy intakes were outside the cut off values. The DFQ indices were energy-adjusted using the residual method [ 45 ].

Participants were also dichotomized as those with and without overweight and obesity based on BMI, WC, and WHtR. Additionally, we compared means and standard deviations for BMI, WC, and WHtR across the Qs of DFQ indices, using a One-way analysis of variance ANOVA. The possible effects of all potential confounding variables that showed a significant relationship with measures of obesity and overweight were adjusted.

The IBM Statistical Package for Social Sciences version 24; SPSS Inc. was used for all statistical analyses. Of the women assessed, Anthropometric measures according to DFQ indices are presented in Table 2.

The ORs of obesity according to quintiles Q of fat types are presented in Table 3. Logistic regression analysis showed a positive association between total SFA and general obesity OR quintiles 5 vs. However, after adjustment for age, asset score, physical activity level, total dietary energy, woman education, husband education, occupation woman, occupation husband, and marital status, the relationship was not remained significant OR quintiles 5 vs.

Additionally, SFA was positively associated with WC in both the unadjusted OR quintiles 5 vs. Also, SFA had a significant positive association with WHtR in the unadjusted model only OR quintiles 5 vs. In the unadjusted model, PUFA showed a positive association with WC OR quintiles 5 vs.

However, in the adjusted model, this relationship was not significant. This relationship attenuated OR quintiles 5 vs. Even after adjustment for covariates, this relationship remained significant.

Moreover, PSR was not associated with obesity parameters. In regard of the AI, a significant increase in the chance of general obesity OR quintiles 5 vs. Similarly, a significant increase in OR for abdominal obesity based on WC OR quintiles 5 vs. When analysis was carried out, in the highest quintile of TI compared with the lowest quintile, the OR increased for general obesity OR quintiles 5 vs.

These observations remained unchanged upon multiple variable adjustments. Additionally, significant positive associations were observed between LI and general obesity OR quintiles 5 vs. In the unadjusted model, we found non-significant positive relationship between TF and abdominal obesity based on both WC and WHtR definitions, however, after multiple variable adjustment, we found significant positive associations between TF and abdominal obesity defined by both WC OR quintiles 5 vs.

The present study evaluated the relationship between general and abdominal obesity, fat types, and indices of dietary fat quality amongst Ghanaian women. A separate assessment of the independent relationships between fatty acid types and obesity have largely failed to show significant associations, except that the total SFA intake only had a positive relationship with WC.

Moreover, abdominal obesity had a positive association with AI, TI, LI, and TF. These associations support the proposition that compared with fat types, the dietary fatty acid indices may show a better relationship with health outcomes [ 30 , 32 ].

The intake of absolute amounts of dietary fatty acids appears not to have a consistent relationship with obesity. For instance, a previous observational study found no association between adiposity and the ω-3 PUFA and SFA intake [ 14 ].

Also, another observational study found an inverse association between dietary SFA and WC after adjustment for age. However, this association was no longer significant after an additional adjustment for BMI [ 46 ].

Unlike the present study, in which the prevalence of obesity was evaluated in association with dietary fatty acids in the form of indices, most previous studies in Ghana only assessed associations between type of cooking fats and oils [ 23 , 47 ] and quantity of dietary fat [ 27 ] in relation to obesity.

Of these studies, no significant association was found between obesity and types of cooking fat and oil [ 23 , 47 ]. These studies are limited in their capacity to reveal the relative proportions of dietary fatty acids that are consumed, therefore, they may not be able to inform nutrition policy formulation.

Moreover, dietary energy intake was not adjusted in these studies. Contrary, Lund et al. The disparity between our findings and Lund et al. study might be explained by the differences in study design, sample size and characteristics of study participants.

In the present study, there was a positive association between TF and abdominal obesity. However, Mogre et al. showed a weak inverse association between the absolute quantity of dietary fat intake and both WC and BMI among University students [ 27 ]. The reasons for the disparity between the present findings and these earlier studies are not understood, however, differences in the study settings may have partly introduced the variations in the results.

Moreover, a setback in those earlier studies was that the effect of total energy intake was not adjusted in the analysis. In the present study, the findings revealed a positive relationship between TI and LI and a chance for both general and abdominal obesity as well as a positive association between AI and TF and abdominal obesity.

It is not entirely understood what accounted for the positive associations observed between obesity and the above DFQ indices, however, it might have resulted from the cumulative contributions of palmitic, myristic and stearic acids and possibly the activities of linoleic acid LA.

This is because higher values of AI, TI and LI favor greater intakes of palmitic, myristic and stearic acids [ 30 , 32 ], which are linked to increased risk of obesity [ 14 ]. Higher intake of PSR was found to associate inversely with general and abdominal obesity although this observation did not attain statistical significance.

The present findings compare partly with the results of two prospective studies [ 12 , 18 ], in which Phillips and colleagues [ 12 ] showed that, low dietary PUFA:SFA ratio at baseline significantly associated with increased risk of both general and abdominal obesity whereas, low dietary PUFA:SFA ratio at follow-up only significantly related with higher increased risk of general obesity but showed non-significant positive association with abdominal obesity.

Although the mechanisms involved in the association of fatty acids and obesity are not clear, there is evidence that long-chain omega fatty acids have the capacity to suppress appetite while prolonging satiety [ 50 ].

Their ingestion also influences gene expressions in various organs that suppress fat deposition, cause an increase in both β-oxidation and energy expenditure [ 51 , 52 ]. Similarly, the medium chain fatty acids are said to cause increased fat oxidation and energy expenditure [ 53 , 54 ]. On the other hand, higher intake of long-chain SFAs such as myristic, palmitic and stearic acids tend to favor higher values of LI, AI and TI [ 31 , 32 ].

They have the ability to influence gene expressions in various organs more especially fat mass and obesity-associated gene and signal transducer and activator of transcription 3 gene polymorphisms [ 12 , 13 , 55 ], thereby, increasing the risk of obesity. Similarly, high ω-6 fatty acids intake may promote the development of obesity.

For instance, evidence from animal studies suggested that when linoleic acid is converted to arachidonic acid, it has the capacity to engineer body weight gain and adipogenesis partly through the prostacyclin pathway [ 56 ]. The present study has important strengths. First, we adjusted for the effects of total energy intake and other important confounding variables in order to estimate independent associations between fat indices and obesity.

Second, as far as we know, this was the first study to investigate DFQ in such a broader form and its relationship with general and abdominal obesity in Ghana; this may serve as precedence for other researchers.

Despite these strengths, the study has some limitations. First, the sample size was small. Therefore, the present results ought to be interpreted with great caution. Third, dietary intake assessment was based on recalls thus, a chance for misreporting of food items cannot be ruled out completely.

Moreover, despite the fact that we adjusted for the effects of dietary energy and other important potential confounders, residual confounding cannot be ruled out. Moreover, the study duration was short.

Hence, we cannot discount seasonal effects on food intake. Furthermore, abdominal obesity showed a positive association with AI and TF.

Additionally, both general and abdominal obesity had positive relationships with TI and LI. The effect of fat quality on obesity should be further studied using an experimental design. The datasets used to support the findings of this study are available from the corresponding author upon request.

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Journal Article. Effects of macronutrient intake in obesity: a meta-analysis of low-carbohydrate and low-fat diets on markers of the metabolic syndrome. Anouk E M Willems , Anouk E M Willems. Groningen Institute for Evolutionary Life Sciences — Neurobiology, University of Groningen.

Van Hall Larenstein University of Applied Sciences, Applied Research Centre Food and Dairy. Oxford Academic. Martina Sura—de Jong. André P van Beek. Department of Endocrinology, University of Groningen, University Medical Center Groningen.

Esther Nederhof. Gertjan van Dijk. van Dijk , Nijenborg 7, AG Groningen, The Netherlands. E-mail: gertjan. dijk rug. PDF Split View Views. Select Format Select format.

ris Mendeley, Papers, Zotero. enw EndNote. bibtex BibTex. txt Medlars, RefWorks Download citation. Permissions Icon Permissions. Close Navbar Search Filter Nutrition Reviews This issue Dietetics and Nutrition Books Journals Oxford Academic Enter search term Search. Abstract The metabolic syndrome MetS comprises cardiometabolic risk factors frequently found in individuals with obesity.

low-fat diet , low-carbohydrate diet , macronutrients , metabolic syndrome , weight loss. Open in new tab Download slide. Age, mean SD years , by diet type. Duration a mo. Food intake assessment. Type of data extracted. Bazzano et al 31 Hu et al 32 OB LC: a Duration of the total study including follow-up.

Open in new tab. Energy intake. Time mo. Energy intake kcal. Fiber intake g. Bazzano et al 31 Hu et al 32 LC ad lib 70 BL Data are reported as mean SD unless otherwise indicated. However, nothing is natural about the trans fats used in processed foods.

These trans fats are produced by adding hydrogen to unsaturated fats to create a product that functions more like a saturated fat. Consuming trans fats can lead to a number of health problems.

Artificial trans fats are linked to inflammation, unhealthy cholesterol changes, impaired artery function, insulin resistance, and excess belly fat 25 , 26 , 27 , 28 , Research has linked the intake of trans fats with a higher risk of cardiovascular disease Trans fats are often found in margarine and other processed spreads.

Food manufacturers sometimes add them to packaged products, such as crackers, to help extend shelf life. Summary: Fats are grouped by the number of bonds in their carbon chains. Aside from trans fats, most fats have beneficial or neutral effects on health. However, a high omega-6 to omega-3 ratio may cause problems.

The appropriate amount of fat to eat will depend on your calorie requirements for weight loss or maintenance. You can use this calculator to determine your calorie needs to lose weight or maintain your weight, which is known as your daily calorie goal.

Here are a few examples of suggested daily fat ranges for a low fat diet, based on different calorie goals:. Studies show higher fat diets, such as low carb and Mediterranean diets, offer many health benefits and may be a better choice than lower fat diets for some people.

A ketogenic diet :. Here are a few examples of suggested daily fat ranges for a low-carb or ketogenic diet, based on different calorie goals:. The Mediterranean diet includes a wide variety of plant and animal foods such as:. Here are a few examples of suggested daily fat ranges for a Mediterranean diet, based on different calorie goals:.

Summary: How much fat you eat per day should be based on the type of diet you follow and your calorie needs for weight loss or maintenance. Fortunately, many delicious foods can provide the fat you need.

Monounsaturated fats are found in most plant and animal foods, but some foods are especially rich in them. Foods rich in omega-3s include:. This can convert to eicosapentaenoic acid EPA and docosahexaenoic acid DHA , which may have health benefits.

However, the conversion rate of ALA to the omega-3s EPA and DHA is poor Summary: Choose a variety of healthy foods that provide fats from each of the different groups every day, especially omega-3 fats. Fats serve a number of important functions, along with making foods taste better and helping you feel satisfied.

Eating the right amounts and right types of fat can go a long way toward reducing disease risk and enhancing your overall health. Our experts continually monitor the health and wellness space, and we update our articles when new information becomes available.

Your body needs dietary fat for many biological processes. Recent research has mostly disproven the notion that eating foods rich in cholesterol and fat may increase your risk of various diseases.

Here are 9…. Here are 14 healthy sources…. While they're not typically able to prescribe, nutritionists can still benefits your overall health. Let's look at benefits, limitations, and more. A new study found that healthy lifestyle choices — including being physically active, eating well, avoiding smoking and limiting alcohol consumption —….

Carb counting is complicated.

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The public health and economic benefits of taxing sugar-sweetened beverages. Wang L, Lee IM, Manson JE, Buring JE, Sesso HD. Alcohol consumption, weight gain, and risk of becoming overweight in middle-aged and older women.

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Rolls, B. Roe, et al. Ello-Martin, J. Ledikwe, et al. Skip to content Obesity Prevention Source. Obesity Prevention Source Menu. Search for:. Home Obesity Definition Why Use BMI? Waist Size Matters Measuring Obesity Obesity Trends Child Obesity Adult Obesity Obesity Consequences Health Risks Economic Costs Obesity Causes Genes Are Not Destiny Prenatal and Early Life Influences Food and Diet Physical Activity Sleep Toxic Food Environment Environmental Barriers to Activity Globalization Obesity Prevention Strategies Families Early Child Care Schools Health Care Worksites Healthy Food Environment Healthy Activity Environment Healthy Weight Checklist Resources and Links About Us Contact Us.

Macronutrients and Weight: Do Carbs, Protein, or Fat Matter? The amount of fat you should eat daily can depend on your total calorie intake. Certain fats may help support weight loss and maintenance. Over the last 50 years, many people have moved from a moderate fat to a low fat diet, based on recommendations from health organizations.

However, the Dietary Guidelines for Americans no longer specifies an upper limit for how much total fat you should consume. This article takes a detailed look at different types of fat and provides suggestions for how much to eat per day.

Along with protein and carbs , fat is one of the three macronutrients in your diet. You consume fat in the form of triglycerides. A triglyceride molecule consists of three fatty acids attached to a glycerol backbone.

The fatty acids contain chains of carbons and hydrogens. One way to classify fats is by the length of their carbon chains :. Most of the fats you eat are long-chain fatty acids. Short-chain fatty acids are mainly produced when bacteria ferment soluble fiber in your colon, although milk fat also contains small amounts.

However, the liver takes up short-chain and medium-chain fats directly and stores them as energy. Summary: Fats are one of the three macronutrients. The body absorbs them from food and uses them for energy and other functions. Summary: Fats provide a number of benefits for your body, including serving as an energy source, regulating hormones and genes, maintaining brain health, and making food tastier and more satisfying.

Fatty acids are grouped according to the number of double bonds between carbons in their structures. MUFA food sources are typically liquid at room temperature and fairly stable for cooking purposes.

The most common MUFA is oleic acid, which olive oil contains in high amounts. Monounsaturated fat is linked to several health benefits, including a reduced risk of serious diseases such as heart disease and diabetes 5 , 6 , 7. One review of 24 controlled studies found diets high in monounsaturated fat lead to significantly lower blood sugar, triglycerides, weight and blood pressure levels, compared to high carb diets.

The high monounsaturated fat diets also increased HDL good cholesterol levels 7. In one study, people felt fuller and took in fewer calories for the next 24 hours after consuming bread alongside oil rich in oleic acid, compared to bread that contained less 8.

They can be divided into groups depending on the location of the double bonds. These include omega-3s and omega-6s. Studies have found that long-chain omega-3 fats have benefits for inflammation, heart disease, diabetes, depression, and other health conditions 9 , 10 , 11 , Although you need some omega-6 fats, they can contribute to chronic inflammation if you consume too much, especially if omega-3 PUFA intake is low 13 , 14 , Omega-6 fats are very common in modern-day diets.

On the other hand, omega-3 fats are usually consumed in much smaller amounts. Significantly, researchers report that the evolutionary diet of humans provided a ratio of omega-6 to omega-3 fats between 1-to-1 and 4-to SFA intake can raise LDL bad cholesterol levels in some people, although this depends in part on the specific fatty acids consumed.

It should also be noted that HDL good cholesterol typically goes up as well For example, studies suggest that the medium-chain triglycerides in coconut oil and palm oil may boost metabolic rate and reduce calorie intake 22 , In a trans fats molecule, hydrogens are positioned across from each other rather than side by side.

Small amounts of trans fats occur naturally in dairy and other animal foods. However, nothing is natural about the trans fats used in processed foods. Some authors have reported that the most important variable influencing meal size is not the level of hunger but the nutrient content of the range of foods consumed.

Thus dietary fat has a weak effect on satiety and we suggest that periodic exposure to a high-fat meal, particularly when hunger is high, may be sufficient to lead to overconsumption of energy as fat in obese patients.

Dietary fat and fat balance: Energy balance is well correlated with fat balance in lean controls, whereas there is no correlation with either carbohydrate or protein balances. Several authors have shown that carbohydrate and protein storage is closely regulated by adjusting oxidation to intake, whereas fat is almost exclusively used or stored in response to day-to-day fluctuations in energy balance.

The positive relationship between fat intake and lipid oxidation seen in lean controls appears not to be present in obese patients.

On high-fat diets, post-obese women failed to increase the ratio of fat to carbohydrate oxidation appropriately.

In this cross-sectional study, dietary information was obtained using h dietary recall. The odds of abdominal obesity based on waist circumference WC were significantly higher among participants in the fifth quintile Q compared with those in the first Q of AI 1.

Similarly, waist to height ratio WHtR was positively associated with AI 2. We also found positive associations between abdominal obesity and AI and TF.

Furthermore, TI and LI showed positive relationships with both general and abdominal obesity. The prevalence of obesity and overweight are increasing globally and if the current trends are not controlled, 2. According to epidemiological studies, the disease burden of obesity is enormous, it increases the risk of infertility in women [ 4 , 5 ], metabolic syndrome [ 6 , 7 ], cardiovascular diseases, type 2 diabetes mellitus T2DM , chronic kidney disease [ 8 ], multiple cancers [ 9 ] and musculoskeletal complications [ 10 , 11 ].

Although genetic factors play a role in the development of obesity [ 12 , 13 ], the contributions of both total dietary fat [ 14 , 15 ] intake and fat types [ 12 , 13 ] have been suggested. However, the relationship between dietary fats and obesity is debatable [ 12 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].

Specifically, positive [ 14 , 15 ] and inverse [ 16 ] associations have been reported between total fat intake and obesity.

Moreover, previous studies suggested both positive [ 12 , 14 ] and inverse [ 17 ] associations between dietary saturated fatty acids SFAs intake and weight gain. Furthermore, both positive [ 13 , 14 ] and negative [ 18 ] relationships between monounsaturated fatty acids MUFAs intake and the chance of obesity have been found.

Additionally, inverse [ 19 , 20 ], null [ 14 , 21 ] and positive [ 14 ] associations have been reported between polyunsaturated fatty acid PUFA consumption and the chance of obesity. Typically, the fats and oils dietary pattern amongst Ghanaians is characterized by palm oil, shea butter and margarine [ 22 , 23 ].

These fats have a high SFA content [ 24 ]. In two cross-sectional studies, this dietary pattern showed null [ 23 ] and unexpectedly inverse associations with general and abdominal obesity in Ghanaian adults [ 27 ]. Evidence from the above discrepant results may suggest that total fat intake and the absolute quantities of individual fatty acids consumed are not sensitive enough to reveal a consistent relationship between dietary fat and obesity.

Therefore, the relative proportions of fatty acids instead of their absolute quantities in the diet may be an enhanced approach to investigate the relationship between dietary fat consumption and prevalence of obesity. In a previous study in French adults, low dietary PUFAs to SFAs ratio was found to accentuate the risk of being generally and abdominally obese [ 12 ].

A major limitation in the African study was that the effect of dietary energy was not accounted for in the analysis [ 18 ], which could seriously confound the results [ 28 , 29 ].

However, in Danish cohort, Lund et al. First, Ulbricht and Southgate [ 30 ], in the early 90s, proposed the atherogenicity index AI and thrombogenicity index TI , which consider the ratio of subclasses of SFAs to MUFAs and PUFAs.

Subsequently, Santos-Silva et al. Later, Ding et al. By considering the predictive effect of excess body weight gain on insulin sensitivity [ 35 ] and the importance of obesity in the development of many other chronic diseases including metabolic syndrome [ 6 ], it is imperative that scientists examine the association between the above fat quality indices and the odds of obesity.

This may be a novel strategy for the development of nutrition policies that are beneficial for the prevention of obesity and excessive weight gain. Participation in this study was voluntary and participants also consented to the study without being coaxed.

We collected data in August and September Based on our pre-defined criteria, potential participants of a reported history of myocardial infarction, renal disease or suffering from other major illnesses diabetes, human immune deficiency virus, renal disease, cardiovascular disease s and malaria were not allowed to take part in the study.

Furthermore, the study excluded women with self-reported severe nausea and vomiting as well as those who were pregnant or lactating prior to recruitment. Moreover, we excluded women who were put on specific diet therapies or had been restricted from consuming a variety of foods.

The study sample size was computed based on a hypothesized correlation coefficient of 0. Based on an established protocol for estimating sample sizes in cross-sectional studies by Rosner [ 36 ], a total of women were first computed. In order to increase the power of the statistical results and to cater for non-response rate the was multiplied by 1.

kg; 22, Hamburg, Germany; Model: ; designed in Germany-made in China. Height measurement was by the use of United Nations Children Emergency Fund height board.

The values were recorded to the nearest 0. We calculated BMI as weight in kilograms divided by the height in meters squared. BMI values of 25— The WC was measured based on World Health Organization guidelines thus, at the mid-point between the lower border of the rib cage and the iliac crest using a non-stretchable fiber-glass measuring tape.

All WC values were recorded to the nearest 0. We calculated WHtR by dividing WC cm by the measured height cm. Information about demographic and lifestyle factors such as age, educational status, marital status, occupation, household size, household assets, parity, and physical activity were obtained using a structured questionnaire following face-to-face interviews.

In order to estimate their economic status, we enumerated household assets for households that the selected study participants belong. Based on the list of items reported and the make-up of their rooms, we calculated a summary value as a proxy for economic status.

Physical activity level of participants was measured using the International Physical Activity Questionnaire short form [ 40 ]. The reliability and validity of these questionnaires were assessed across 12 countries and the results showed they can be applied in many settings and in different languages [ 41 ].

All dietary data were obtained based on repeated non-consecutive 2-day h dietary recalls. We used real food items, food models and standard kitchen weighing equipment to guide food portion size estimation. We deployed a trained caterer for the interviews.

Absolute quantities of dietary fatty acids were determined using standard portion sizes of food items from the United States Department of Agriculture Food Composition Databases [ 42 ] due to a lack of local database on this issue.

For the same reason, we obtained melting points of dietary fatty acids from the Japanese Lipid Bank database [ 43 ]. The mean values of daily intake of total fat, fatty acids, and energy for the two days were used in the analysis.

Also, we calculated DFQ indices from established protocols as detailed below. The lipophilic index of dietary intake was computed by multiplying the intake of each fatty acid in grams by its specific melting point °C , adding the products, and then dividing by the total of fatty acid intake in grams [ 34 ].

All data were checked for compliance with the selected statistical techniques. The Kolmogorov—Smirnov test was used to evaluate the normality of the data distribution. To use physical activity as a continuous variable in our regression models, it was first transformed natural logarithm because it was positively skewed.

Out of the subjects sampled for the study, we excluded 18 participants because their reported dietary energy intakes were outside the cut off values. The DFQ indices were energy-adjusted using the residual method [ 45 ]. Participants were also dichotomized as those with and without overweight and obesity based on BMI, WC, and WHtR.

Additionally, we compared means and standard deviations for BMI, WC, and WHtR across the Qs of DFQ indices, using a One-way analysis of variance ANOVA. The possible effects of all potential confounding variables that showed a significant relationship with measures of obesity and overweight were adjusted.

The IBM Statistical Package for Social Sciences version 24; SPSS Inc. was used for all statistical analyses. Of the women assessed, Anthropometric measures according to DFQ indices are presented in Table 2.

The ORs of obesity according to quintiles Q of fat types are presented in Table 3. Logistic regression analysis showed a positive association between total SFA and general obesity OR quintiles 5 vs.

However, after adjustment for age, asset score, physical activity level, total dietary energy, woman education, husband education, occupation woman, occupation husband, and marital status, the relationship was not remained significant OR quintiles 5 vs.

Additionally, SFA was positively associated with WC in both the unadjusted OR quintiles 5 vs. Also, SFA had a significant positive association with WHtR in the unadjusted model only OR quintiles 5 vs. In the unadjusted model, PUFA showed a positive association with WC OR quintiles 5 vs.

However, in the adjusted model, this relationship was not significant. This relationship attenuated OR quintiles 5 vs. Even after adjustment for covariates, this relationship remained significant.

Moreover, PSR was not associated with obesity parameters. In regard of the AI, a significant increase in the chance of general obesity OR quintiles 5 vs. Similarly, a significant increase in OR for abdominal obesity based on WC OR quintiles 5 vs.

When analysis was carried out, in the highest quintile of TI compared with the lowest quintile, the OR increased for general obesity OR quintiles 5 vs. These observations remained unchanged upon multiple variable adjustments.

Additionally, significant positive associations were observed between LI and general obesity OR quintiles 5 vs. In the unadjusted model, we found non-significant positive relationship between TF and abdominal obesity based on both WC and WHtR definitions, however, after multiple variable adjustment, we found significant positive associations between TF and abdominal obesity defined by both WC OR quintiles 5 vs.

The present study evaluated the relationship between general and abdominal obesity, fat types, and indices of dietary fat quality amongst Ghanaian women. A separate assessment of the independent relationships between fatty acid types and obesity have largely failed to show significant associations, except that the total SFA intake only had a positive relationship with WC.

Moreover, abdominal obesity had a positive association with AI, TI, LI, and TF. These associations support the proposition that compared with fat types, the dietary fatty acid indices may show a better relationship with health outcomes [ 30 , 32 ]. The intake of absolute amounts of dietary fatty acids appears not to have a consistent relationship with obesity.

For instance, a previous observational study found no association between adiposity and the ω-3 PUFA and SFA intake [ 14 ]. Also, another observational study found an inverse association between dietary SFA and WC after adjustment for age. However, this association was no longer significant after an additional adjustment for BMI [ 46 ].

Unlike the present study, in which the prevalence of obesity was evaluated in association with dietary fatty acids in the form of indices, most previous studies in Ghana only assessed associations between type of cooking fats and oils [ 23 , 47 ] and quantity of dietary fat [ 27 ] in relation to obesity.

Of these studies, no significant association was found between obesity and types of cooking fat and oil [ 23 , 47 ]. These studies are limited in their capacity to reveal the relative proportions of dietary fatty acids that are consumed, therefore, they may not be able to inform nutrition policy formulation.

Moreover, dietary energy intake was not adjusted in these studies. Contrary, Lund et al. The disparity between our findings and Lund et al. study might be explained by the differences in study design, sample size and characteristics of study participants. In the present study, there was a positive association between TF and abdominal obesity.

However, Mogre et al. showed a weak inverse association between the absolute quantity of dietary fat intake and both WC and BMI among University students [ 27 ]. The reasons for the disparity between the present findings and these earlier studies are not understood, however, differences in the study settings may have partly introduced the variations in the results.

Moreover, a setback in those earlier studies was that the effect of total energy intake was not adjusted in the analysis. In the present study, the findings revealed a positive relationship between TI and LI and a chance for both general and abdominal obesity as well as a positive association between AI and TF and abdominal obesity.

It is not entirely understood what accounted for the positive associations observed between obesity and the above DFQ indices, however, it might have resulted from the cumulative contributions of palmitic, myristic and stearic acids and possibly the activities of linoleic acid LA.

This is because higher values of AI, TI and LI favor greater intakes of palmitic, myristic and stearic acids [ 30 , 32 ], which are linked to increased risk of obesity [ 14 ]. Higher intake of PSR was found to associate inversely with general and abdominal obesity although this observation did not attain statistical significance.

The present findings compare partly with the results of two prospective studies [ 12 , 18 ], in which Phillips and colleagues [ 12 ] showed that, low dietary PUFA:SFA ratio at baseline significantly associated with increased risk of both general and abdominal obesity whereas, low dietary PUFA:SFA ratio at follow-up only significantly related with higher increased risk of general obesity but showed non-significant positive association with abdominal obesity.

Although the mechanisms involved in the association of fatty acids and obesity are not clear, there is evidence that long-chain omega fatty acids have the capacity to suppress appetite while prolonging satiety [ 50 ]. Their ingestion also influences gene expressions in various organs that suppress fat deposition, cause an increase in both β-oxidation and energy expenditure [ 51 , 52 ].

Similarly, the medium chain fatty acids are said to cause increased fat oxidation and energy expenditure [ 53 , 54 ]. On the other hand, higher intake of long-chain SFAs such as myristic, palmitic and stearic acids tend to favor higher values of LI, AI and TI [ 31 , 32 ].

They have the ability to influence gene expressions in various organs more especially fat mass and obesity-associated gene and signal transducer and activator of transcription 3 gene polymorphisms [ 12 , 13 , 55 ], thereby, increasing the risk of obesity. Similarly, high ω-6 fatty acids intake may promote the development of obesity.

For instance, evidence from animal studies suggested that when linoleic acid is converted to arachidonic acid, it has the capacity to engineer body weight gain and adipogenesis partly through the prostacyclin pathway [ 56 ].

The present study has important strengths. First, we adjusted for the effects of total energy intake and other important confounding variables in order to estimate independent associations between fat indices and obesity. Second, as far as we know, this was the first study to investigate DFQ in such a broader form and its relationship with general and abdominal obesity in Ghana; this may serve as precedence for other researchers.

Despite these strengths, the study has some limitations. First, the sample size was small. Therefore, the present results ought to be interpreted with great caution.

Third, dietary intake assessment was based on recalls thus, a chance for misreporting of food items cannot be ruled out completely. Moreover, despite the fact that we adjusted for the effects of dietary energy and other important potential confounders, residual confounding cannot be ruled out.

Moreover, the study duration was short. Hence, we cannot discount seasonal effects on food intake. Furthermore, abdominal obesity showed a positive association with AI and TF. Additionally, both general and abdominal obesity had positive relationships with TI and LI.

The effect of fat quality on obesity should be further studied using an experimental design. The datasets used to support the findings of this study are available from the corresponding author upon request.

Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in and projections to Int J Obes. Article CAS Google Scholar. Stevens GA, Singh GM, Lu Y, Danaei G, Lin JK, Finucane MM, et al. Over the last 50 years, many people have moved from a moderate fat to a low fat diet, based on recommendations from health organizations.

However, the Dietary Guidelines for Americans no longer specifies an upper limit for how much total fat you should consume. This article takes a detailed look at different types of fat and provides suggestions for how much to eat per day.

Along with protein and carbs , fat is one of the three macronutrients in your diet. You consume fat in the form of triglycerides.

A triglyceride molecule consists of three fatty acids attached to a glycerol backbone. The fatty acids contain chains of carbons and hydrogens. One way to classify fats is by the length of their carbon chains :.

Most of the fats you eat are long-chain fatty acids. Short-chain fatty acids are mainly produced when bacteria ferment soluble fiber in your colon, although milk fat also contains small amounts. However, the liver takes up short-chain and medium-chain fats directly and stores them as energy.

Summary: Fats are one of the three macronutrients. The body absorbs them from food and uses them for energy and other functions. Summary: Fats provide a number of benefits for your body, including serving as an energy source, regulating hormones and genes, maintaining brain health, and making food tastier and more satisfying.

Fatty acids are grouped according to the number of double bonds between carbons in their structures. MUFA food sources are typically liquid at room temperature and fairly stable for cooking purposes.

The most common MUFA is oleic acid, which olive oil contains in high amounts. Monounsaturated fat is linked to several health benefits, including a reduced risk of serious diseases such as heart disease and diabetes 5 , 6 , 7.

One review of 24 controlled studies found diets high in monounsaturated fat lead to significantly lower blood sugar, triglycerides, weight and blood pressure levels, compared to high carb diets.

The high monounsaturated fat diets also increased HDL good cholesterol levels 7. In one study, people felt fuller and took in fewer calories for the next 24 hours after consuming bread alongside oil rich in oleic acid, compared to bread that contained less 8.

They can be divided into groups depending on the location of the double bonds. These include omega-3s and omega-6s. Studies have found that long-chain omega-3 fats have benefits for inflammation, heart disease, diabetes, depression, and other health conditions 9 , 10 , 11 , Although you need some omega-6 fats, they can contribute to chronic inflammation if you consume too much, especially if omega-3 PUFA intake is low 13 , 14 , Omega-6 fats are very common in modern-day diets.

On the other hand, omega-3 fats are usually consumed in much smaller amounts. Significantly, researchers report that the evolutionary diet of humans provided a ratio of omega-6 to omega-3 fats between 1-to-1 and 4-to SFA intake can raise LDL bad cholesterol levels in some people, although this depends in part on the specific fatty acids consumed.

It should also be noted that HDL good cholesterol typically goes up as well For example, studies suggest that the medium-chain triglycerides in coconut oil and palm oil may boost metabolic rate and reduce calorie intake 22 , In a trans fats molecule, hydrogens are positioned across from each other rather than side by side.

Small amounts of trans fats occur naturally in dairy and other animal foods. However, nothing is natural about the trans fats used in processed foods. These trans fats are produced by adding hydrogen to unsaturated fats to create a product that functions more like a saturated fat.

Consuming trans fats can lead to a number of health problems. The positive relationship between fat intake and lipid oxidation seen in lean controls appears not to be present in obese patients.

On high-fat diets, post-obese women failed to increase the ratio of fat to carbohydrate oxidation appropriately. Increasing dietary fat results in preferential fat storage in post-obese women, impaired suppression of carbohydrate and reduction of 24h energy expenditure.

Conclusions: Dietary fat induces overconsumption and weight gain through its low satiety properties and high caloric density. Obese and post-obese subjects do not appear to adapt to dietary fat, and therefore fat storage is increased. Abstract Epidemiology: Epidemiological evidence suggests that a high-fat diet promotes the development of obesity and that there is a direct relationship between the amount of dietary fat and the degree of obesity.